R Notebook
NodeNumber analysis
library(plyr)-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
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Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
herelibrary(dplyr)
library(ggplot2)
library(lubridate)
library(hydroGOF)
library(xtable)
library(knitr)
library(tidyr)
library(RSQLite)
library(agricolae)
library(scales)
library(zoo)
detach(package:plyr)### load obs data
upDir <- "D:/R/CombinedData/"
obsData <- "D:/R/CombinedData/"
obsAll <- read.table(paste0(obsData, "ObsAll.txt"),
header = TRUE)
obsA1<- obsAll %>%
dplyr::filter(Collection=="2000_2002")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(ifelse(Rotation=="1",paste0("S",Rotation),paste0("R",Rotation))))
obsA4<- obsAll %>%
dplyr::filter(Collection=="2014_2018")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(ifelse(Rotation=="1",paste0("S",Rotation),paste0("R",Rotation))))
obsA2<- obsAll %>%
dplyr::filter(Collection=="1997_2001")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(paste0("R",Rotation)))
obsA3<- obsAll %>%
dplyr::filter(Collection=="2002_2004")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(paste0("R",Rotation)))
obsA<-rbind(obsA2,obsA3,obsA1,obsA4)
summary(obsA) Clock.Today Name Collection Experiment
Min. :1997-01-28 Iversen_91DefoliationLL :895 1997_2001: 955 Lincoln2000:1218
1st Qu.:2001-02-10 Iversen_121DefoliationLLFDFD5 :777 2000_2002:1067 Lincoln2015:1132
Median :2002-08-22 Iversen_8Waterirr :494 2002_2004: 895 Lincoln2001: 387
Mean :2005-09-21 Iversen_8Waterdry :461 2010_2012: 0 Lincoln2003: 384
3rd Qu.:2015-03-09 Iversen_121DefoliationHHFDFD5 :288 2014_2018:1134 Lincoln2002: 257
Max. :2018-01-15 Iversen_9SowingDateSD1Waterirr:277 Lincoln2004: 254
(Other) :859 (Other) : 419
Water Defoliation SowingDate FD Period GrowthSeason Rotation
dry: 973 HH: 288 No :1850 FD10: 35 00_01 :995 Min. :1.000 Min. :1.000
irr:3078 LL:3763 no :1134 FD2 : 34 01_02 :522 1st Qu.:1.000 1st Qu.:1.000
SD1 : 536 FD5 :3982 02_03 :432 Median :2.000 Median :3.000
SD2 : 199 03_04 :412 Mean :2.277 Mean :2.983
SD3 : 169 15_16 :410 3rd Qu.:3.000 3rd Qu.:4.000
SD4 : 163 16_17 :342 Max. :6.000 Max. :7.000
(Other): 0 (Other):938
StartDate MidDate FinishDate Interval Variable
Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00 NodeNumber :792
1st Qu.:2000-12-27 1st Qu.:2001-02-10 1st Qu.:2001-03-06 1st Qu.: 19.00 Height :697
Median :2002-06-14 Median :2002-08-09 Median :2002-10-05 Median : 32.00 shootbiomass:585
Mean :2005-08-15 Mean :2005-09-14 Mean :2005-10-15 Mean : 36.97 LAI :538
3rd Qu.:2015-01-30 3rd Qu.:2015-03-13 3rd Qu.:2015-04-24 3rd Qu.: 48.00 SWC :284
Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00 Fraction :220
(Other) :935
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 220 12:00:00:4051 Min. : 0.00 Min. : 0.0000 E1: 955 ILL :1389
cm : 454 1st Qu.: 3.50 1st Qu.: 0.0000 E2:1067 ILLF5 : 777
fractio0l: 833 Median : 12.47 Median : 0.2582 E3: 895 DLL : 461
Fraction : 434 Mean : 585.40 Mean : 88.3883 E4: 0 IHHF5 : 288
kg/ha :1045 3rd Qu.: 623.15 3rd Qu.: 30.3450 E5:1134 ILLS1 : 277
m2/m2 : 538 Max. :8556.38 Max. :2532.5264 DLLS1 : 259
mm : 527 NA's :1192 (Other): 600
ID GrowthSeason1 Rotation1 ExpUnitCode
E3ILL :895 Gs_1:1352 Rt_1:1175 Iversen_91DefoliationLLGs_2Rt_1: 84
E5ILLF5:777 Gs_2:1447 Rt_2: 823 Iversen_91DefoliationLLGs_1Rt_1: 72
E1ILL :494 Gs_3: 530 Rt_3: 613 Iversen_91DefoliationLLGs_1Rt_4: 67
E1DLL :461 Gs_4: 272 Rt_4: 458 Iversen_91DefoliationLLGs_1Rt_5: 67
E5IHHF5:288 Gs_5: 399 Rt_5: 443 Iversen_91DefoliationLLGs_1Rt_3: 66
E2ILLS1:277 Gs_6: 51 Rt_6: 397 Iversen_91DefoliationLLGs_1Rt_6: 62
(Other):859 Rt_7: 142 (Other) :3633
Clock.Today1 GrowthSeason2 Rotation2
Min. :1997-01-28 12:00:00 Y1(00_01): 596 R2 :823
1st Qu.:2001-02-10 12:00:00 Y1(02_03): 432 S1 :768
Median :2002-08-22 12:00:00 Y2(01_02): 428 R3 :613
Mean :2005-09-21 17:05:34 Y2(03_04): 412 R4 :458
3rd Qu.:2015-03-09 12:00:00 Y2(15_16): 410 R5 :443
Max. :2018-01-15 12:00:00 Y5(00_01): 399 R1 :407
(Other) :1374 (Other):539 obsAupDir <- "D:/R/"
obsData <- "D:/R/TtAll/"
Tt<- read.table(paste0(obsData, "df.all.txt"),
header = TRUE)
TtA <- Tt %>% mutate(Clock.Today=dmy(Clock.Today), ExpUnitCode=as.factor(ExpName))
TtAObsH <-merge(obsA,TtA,by=c("Clock.Today","ExpUnitCode")) %>%
mutate(GrowthRotation=as.factor(paste0(GrowthSeason.x,Rotation.x)))%>%
dplyr::filter(Water.x=="irr")%>%
dplyr::filter(Defoliation.x=="LL")%>%
dplyr::filter(Variable=="NodeNumber")
summary(ObsH) Clock.Today ExpUnitCode Name
Min. :1997-10-23 Iversen_121DefoliationLLFDFD5Gs_1Rt_1 : 85 Iversen_121DefoliationLLFDFD5 :605
1st Qu.:2000-12-23 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 60 Iversen_8Waterirr :605
Median :2002-01-08 Iversen_8WaterirrGs_4Rt_5 : 55 Iversen_91DefoliationLL :455
Mean :2005-06-12 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 55 Iversen_9SowingDateSD1Waterirr:300
3rd Qu.:2014-11-30 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 55 Iversen_9SowingDateSD2Waterirr:110
Max. :2018-01-15 Iversen_8WaterirrGs_5Rt_1 : 50 Iversen_9SowingDateSD3Waterirr:100
(Other) :1895 (Other) : 80
Collection Experiment.x Water.x Defoliation.x SowingDate.x FD.x Period
1997_2001:605 Lincoln2000:755 dry: 0 HH: 0 No :1060 FD10: 0 00_01 :630
2000_2002:590 Lincoln2015:605 irr:2255 LL:2255 no : 605 FD2 : 0 99_00 :230
2002_2004:455 Lincoln2003:225 SD1 : 300 FD5 :2255 02_03 :215
2010_2012: 0 Lincoln2001:185 SD2 : 110 03_04 :205
2014_2018:605 Lincoln1999:140 SD3 : 100 16_17 :195
Lincoln2004:140 SD4 : 80 15_16 :175
(Other) :205 (Other): 0 (Other):605
GrowthSeason.x Rotation.x StartDate MidDate FinishDate
Min. :1.000 Min. :1.000 Min. :1997-10-07 Min. :1997-10-28 Min. :1997-11-19
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2000-11-14 1st Qu.:2000-12-29 1st Qu.:2001-02-13
Median :2.000 Median :3.000 Median :2001-12-22 Median :2002-01-16 Median :2002-02-11
Mean :2.317 Mean :3.118 Mean :2005-05-10 Mean :2005-06-08 Mean :2005-07-08
3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:2014-11-03 3rd Qu.:2014-12-16 3rd Qu.:2015-01-29
Max. :5.000 Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15
Interval Variable VariableUnits Time Observed StdDEV
Min. : 0.00 NodeNumber:2255 % : 0 12:00:00:2255 Min. : 0.000 Min. :0.0000
1st Qu.: 17.00 Branch : 0 cm : 0 1st Qu.: 5.100 1st Qu.:0.0000
Median : 29.00 CrownWt : 0 fractio0l:1650 Median : 7.800 Median :0.2600
Mean : 32.86 Fraction : 0 Fraction : 605 Mean : 7.668 Mean :0.3549
3rd Qu.: 40.00 HardStemWt: 0 kg/ha : 0 3rd Qu.:10.400 3rd Qu.:0.5774
Max. :116.00 Height : 0 m2/m2 : 0 Max. :16.667 Max. :2.1000
(Other) : 0 mm : 0 NA's :520
ExperimentID TreatmentID ID GrowthSeason1 Rotation1 Clock.Today1
E1:605 ILL :1060 E1ILL :605 Gs_1:805 Rt_1:615 Min. :1997-10-23 12:00:00
E2:590 ILLF5 : 605 E5ILLF5:605 Gs_2:645 Rt_2:430 1st Qu.:2000-12-23 12:00:00
E3:455 ILLS1 : 300 E3ILL :455 Gs_3:300 Rt_3:325 Median :2002-01-08 12:00:00
E4: 0 ILLS2 : 110 E2ILLS1:300 Gs_4:295 Rt_4:275 Mean :2005-06-12 20:46:57
E5:605 ILLS3 : 100 E2ILLS2:110 Gs_5:210 Rt_5:265 3rd Qu.:2014-11-30 12:00:00
ILLS4 : 80 E2ILLS3:100 Gs_6: 0 Rt_6:260 Max. :2018-01-15 12:00:00
(Other): 0 (Other): 80 Rt_7: 85
GrowthSeason2 Rotation2 year day rain maxt
Y1(00_01):420 R2 :430 Min. :1997 Min. : 1.0 Min. : 0.0000 Min. : 7.90
Y4(99_00):230 S1 :420 1st Qu.:2000 1st Qu.: 63.0 1st Qu.: 0.0000 1st Qu.:15.10
Y1(02_03):215 R3 :325 Median :2002 Median :138.0 Median : 0.0000 Median :18.80
Y5(00_01):210 R4 :275 Mean :2005 Mean :171.9 Mean : 0.9233 Mean :19.02
Y2(03_04):205 R5 :265 3rd Qu.:2014 3rd Qu.:297.0 3rd Qu.: 0.0000 3rd Qu.:22.00
Y3(16_17):195 R6 :260 Max. :2018 Max. :365.0 Max. :35.3000 Max. :33.80
(Other) :780 (Other):280
mint mean radn wind vp Pp
Min. :-4.90 Min. : 2.55 Min. : 1.30 Min. :0.600 Min. : 5.10 Min. :10.02
1st Qu.: 4.70 1st Qu.:10.50 1st Qu.: 9.70 1st Qu.:2.800 1st Qu.:10.08 1st Qu.:12.15
Median : 8.60 Median :13.20 Median :16.40 Median :3.900 Median :11.60 Median :14.28
Mean : 7.97 Mean :13.49 Mean :16.89 Mean :3.923 Mean :11.91 Mean :13.96
3rd Qu.:11.30 3rd Qu.:16.50 3rd Qu.:23.10 3rd Qu.:4.800 3rd Qu.:14.00 3rd Qu.:15.92
Max. :20.60 Max. :26.20 Max. :34.50 Max. :9.300 Max. :22.90 Max. :16.65
Tbb TTbroken TTfick TTbeta
Min. :1 Min. : 0.6683 Min. : 0.6683 Min. : 0.03021
1st Qu.:2 1st Qu.: 6.3519 1st Qu.: 6.9410 1st Qu.: 2.12367
Median :3 Median : 8.7409 Median : 9.6773 Median : 3.87643
Mean :3 Mean : 9.1513 Mean : 9.8401 Mean : 5.43220
3rd Qu.:4 3rd Qu.:11.7322 3rd Qu.:12.7247 3rd Qu.: 7.68109
Max. :5 Max. :20.2576 Max. :20.8129 Max. :23.34334
ExpName Experiment.y Water.y Defoliation.y SowingDate.y
Iversen_121DefoliationLLFDFD5Gs_1Rt_1 : 85 Lincoln1997:605 dry: 0 HH: 0 No :1060
Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 60 Lincoln2000:510 irr:2255 LL:2255 no : 605
Iversen_8WaterirrGs_4Rt_5 : 55 Lincoln2001: 80 LS: 0 SD1 : 300
Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 55 Lincoln2002:455 SL: 0 SD2 : 110
Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 55 Lincoln2003: 0 SS: 0 SD3 : 100
Iversen_8WaterirrGs_5Rt_1 : 50 Lincoln2010: 0 SD4 : 80
(Other) :1895 Lincoln2015:605 (Other): 0
FD.y GrowthSeason.y Rotation.y Tt_beta_sum Tt_fick_sum Tt_broken_sum
FD10: 0 Gs_1:805 Rt_1 :615 Min. : 0.0967 Min. : 1.36 Min. : 1.36
FD2 : 0 Gs_2:645 Rt_2 :430 1st Qu.: 63.1925 1st Qu.: 162.47 1st Qu.: 151.24
FD5 :2255 Gs_3:300 Rt_3 :325 Median :122.2336 Median : 274.04 Median : 254.09
FD6 : 0 Gs_4:295 Rt_4 :275 Mean :152.1671 Mean : 298.79 Mean : 276.59
Gs_5:210 Rt_5 :265 3rd Qu.:211.0533 3rd Qu.: 401.50 3rd Qu.: 372.81
Gs_6: 0 Rt_6 :260 Max. :722.6708 Max. :1186.65 Max. :1051.88
(Other): 85
Ppm Tmean GrowthRotation
Min. :10.25 Min. : 7.024 11 : 330
1st Qu.:11.99 1st Qu.:11.337 12 : 190
Median :14.52 Median :13.963 13 : 115
Mean :13.89 Mean :13.282 21 : 115
3rd Qu.:15.99 3rd Qu.:15.771 26 : 115
Max. :16.55 Max. :19.327 22 : 110
(Other):1280 mytheme1<-theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14,angle=90, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
mytheme3<-theme(
legend.title = element_text(colour="black", size=14, face="bold"),
legend.text = element_text(colour="black", size = 14,face="plain"),
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14),
legend.position = "right", legend.box = "vertical")Examples for order text
x1 <- c("white","white","blue","green","red","blue","red")
ord <- c("red","white","blue","green")
f1 <- factor(x1,levels=ord)
sort(f1)[1] red red white white blue blue green
Levels: red white blue greenx2 <- as.character(sort(f1))
x <- c("white","white","blue","green","red","blue","red")
y <- c("red","white","blue","green")
x[order(match(x, y))][1] "red" "red" "white" "white" "blue" "blue" "green"obsNode<-ObsH%>%
dplyr::filter(Tbb==1)
obsNode0<-obsNode%>%
dplyr::filter(Name=="Iversen_8Waterirr")
obsNode0$Rotation2<- factor(obsNode0$Rotation2, levels=c("R1", "R2", "R3", "R4", "R5","R6", "R7"))
obsNode0%>%
ggplot(aes(x=Tt_broken_sum, y=Observed))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd) ")+ylab("Node number")+ggtitle(paste0("E1ILL","(Iversen_8Waterirr)")) +
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+mytheme1
#to save a final graph
#ggsave("D:/R/Pictures/Node/Iversen_8Waterirrnode.png", width=8, height=6, dpi=500)#fig.height=4, fig.width=8
obsNode1<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD1Waterirr")
obsNode1$Rotation2<- factor(obsNode1$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode1%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd) ")+scale_x_continuous(limits=c(0,425))+
ylab("Node number")+ggtitle(paste0("E2ILLS1","(Iversen_9SowingDateSD1Waterirr)"))+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+
theme(axis.title.y=element_text(face="bold",colour="black",size = 14))+mytheme1Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD1Waterirrnode.png", width=8, height=4, dpi=500)
obsNode2<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD2Waterirr")
obsNode2$Rotation2<- factor(obsNode2$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode2%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+ggtitle(paste0("E2ILLS2","(Iversen_9SowingDateSD2Waterirr)"))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD2Waterirrnode.png", width=8, height=3, dpi=500)
obsNode1<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD3Waterirr")
obsNode1$Rotation2<- factor(obsNode1$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode1%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+ggtitle("E2ILLS3(Iversen_9SowingDateSD3Waterirr)")+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 14))+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD3Waterirrnode.png", width=8, height=3, dpi=500)
obsNode1<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD4Waterirr")
obsNode1$Rotation2<- factor(obsNode1$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode1%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+ggtitle("E2ILLS4(Iversen_9SowingDateSD4Waterirr)")+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD4Waterirrnode.png", width=8, height=3, dpi=500)
obsNode4<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
obsNode4$Rotation2<- factor(obsNode4$Rotation2, levels=c("R1", "R2", "R3", "R4", "R5","R6","R7"))
obsNode4%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+
ggtitle("E3ILL(Iversen_91DefoliationLL)")+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+
mytheme1
#ggsave("D:/R/Pictures/Node/Iversen_91DefoliationLLnode.png", width=8, height=5, dpi=500)
# ggsave("D:/R/Pictures/Poster/nodenumberE3.png", width=8, height=4, dpi=500)obsNodeLL<-obsNode%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")
obsNodeLL$Rotation2<- factor(obsNodeLL$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6","R7"))
obsNodeLL%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+scale_x_continuous(limits=c(0,430))+
ylab("Node number")+ggtitle("E5ILLF5(Iverson_121DefoliationLLFDFD5)")+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
facet_grid(GrowthSeason2~Rotation2)+
mytheme1Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
# ggsave("D:/R/Pictures/Node/Iversen_121DefoliationLLFDFD5node.png", width=8, height=6, dpi=500)R squared and phyllo calculation for each rotation
TTWES1
Tb evaluation
#obsef1%>%
# dplyr::filter(Ture!="No")
# statWE1<-ObsH%>%
# filter(Variable == "NodeNumber") %>%
# filter(Water=="irr")%>%
# filter(Defoliation=="LL")%>%
# mutate(GrowthSeason=factor(GrowthSeason))%>%
# group_by(GrowthSeason,Collection) %>%
uniTb <- unique(ObsH$Tbb)
PhyBeta.all <- data.frame()
for(i in 1:length(uniTb)){
ObsH1 <- ObsH %>%
filter(Tbb==uniTb[i])
ObsH.sub <- ObsH1 %>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,Tbb,ID,ExperimentID) %>%
do(mod.beta = lm(Tt_beta_sum~Observed,data=.))%>%
mutate(R2WE = summary(mod.beta)$r.squared)%>%
mutate(PhylloWE = summary(mod.beta)$coeff[2])%>%
dplyr::select(-mod.beta)
PhyBeta.all <- rbind(PhyBeta.all, ObsH.sub)
}
PhyBeta.allwrite.csv(PhyBeta.all,"D:/R/PhyllochronData/PhyBeta.all.csv", row.names = FALSE)uniTb <- unique(ObsH$Tbb)
Phybroken.all <- data.frame()
for(i in 1:length(uniTb)){
ObsH1 <- ObsH %>%
filter(Tbb==uniTb[i])
ObsH.sub <- ObsH1 %>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,Tbb,ID,ExperimentID) %>%
do(mod.broken = lm(Tt_broken_sum~Observed,data=.))%>%
#do(mod.fick = lm(Tt_fick_sum~Observed,data=.))%>%
mutate(R2Br = summary(mod.broken)$r.squared)%>%
mutate(PhylloBr = summary(mod.broken)$coeff[2])%>%
mutate(P=anova(mod.broken)$'Pr(>F)'[1])%>%
mutate(intcp= summary(mod.broken)$coeff[1])%>%
dplyr::select(-mod.broken)
Phybroken.all <- rbind(Phybroken.all, ObsH.sub)
}ANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliable
Phybroken.allwrite.csv(Phybroken.all,"D:/R/PhyllochronData/Phybroken.all.csv", row.names = FALSE)uniTb <- unique(ObsH$Tbb)
Phyfick.all <- data.frame()
for(i in 1:length(uniTb)){
ObsH1 <- ObsH %>%
filter(Tbb==uniTb[i])
ObsH.sub <- ObsH1 %>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,Tbb,ID,ExperimentID) %>%
do(mod.fick = lm(Tt_fick_sum~Observed,data=.))%>%
mutate(R2fi = summary(mod.fick)$r.squared)%>%
mutate(Phyllofick = summary(mod.fick)$coeff[2])%>%
dplyr::select(-mod.fick)
Phyfick.all <- rbind(Phyfick.all, ObsH.sub)
}
Phyfick.allwrite.csv(Phyfick.all,"D:/R/PhyllochronData/Phyfick.all.csv", row.names = FALSE)Combine the Phyllo data together
multmerge = function(mypath){
filenames=list.files(path=mypath, full.names=TRUE)
datalist = lapply(filenames, function(x){read.csv(file=x,header=T)})
Reduce(function(x,y) {merge(x,y)}, datalist)}
mergephy=multmerge("D:/R/PhyllochronData")
mergephymergeP<-mergephy%>%
dplyr::select(Name,GrowthSeason.x,Rotation.x,Collection,Tmean, Ppm,Tbb, PhylloWE,PhylloBr,Phyllofick,ID,ExperimentID) %>%
tidyr::gather("Variable","Phyllo",PhylloWE:Phyllofick)%>%
mutate(PhylloTb=as.factor(paste0(Variable,Tbb)))%>%
mutate(GrowthRotation=as.factor(paste0(GrowthSeason.x,Rotation.x)))
unique(mergeP)mergeP$Names[mergeP$Name=="Iversen_8Waterirr" ] <-"I8irr"
mergeP$Names[mergeP$Name=="Iversen_91DefoliationLL"] <-"I9LL"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD1Waterirr"]<-"I9SD1irr"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD2Waterirr"]<-"I9SD2irr"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD3Waterirr"]<-"I9SD3irr"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD4Waterirr"]<-"I9SD4irr"
mergeP$Names[mergeP$Name=="Iversen_121DefoliationLLFDFD5"]<-"I12DF5"
mergePRegression coefficient
mergeP%>%
ggplot(aes(x=Tmean, y=Phyllo, colour=factor(Names)))+geom_point(size=2)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+xlab("Mean air temperature (°C)")+ylab("Phyllochron (°Cd/Leaf)")+
facet_wrap(~PhylloTb,ncol = 2)+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAuniPhy <- unique(mergeP$PhylloTb)
Slope.df<-data.frame()
for(i in 1:length(uniPhy) ){
mergeP.sub <- mergeP %>%
filter(PhylloTb==uniPhy[i])%>%
group_by(PhylloTb) %>%
do(modPhy = lm(Tmean~Phyllo,data=.))%>%
mutate(R2 = summary(modPhy)$r.squared)%>%
mutate(slope = summary(modPhy)$coeff[2])%>%
mutate(P=anova(modPhy)$'Pr(>F)'[1])%>%
dplyr::select(-modPhy)
Slope.df <- rbind(Slope.df,mergeP.sub)
}
Slope.dfCV coeffecient of variation
mergePmergePcv<-mergeP%>%group_by(Collection, Variable)%>%
mutate(meanPhyllo=mean(Phyllo))%>%
mutate(SDPhyllo=sd(Phyllo))%>%
mutate(CVPhyllo=SDPhyllo/meanPhyllo)
mergePcvmergePcv1<-mergePcv%>%group_by(Variable)%>%
mutate(CVmean=mean(CVPhyllo))
mergePcv%>%
ggplot(aes(x=PhylloTb,y=CVPhyllo,color=factor(Collection)))+geom_point(size=2)+theme_bw()
mergePcv1%>%
ggplot(aes(x=PhylloTb,y=CVmean))+geom_point(size=2)+theme_bw()
mergePcv1x-intercept method
LAR2<-ObsH%>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,GrowthRotation,ExperimentID,ID) %>%
do(mod = lm(Observed~Interval,data=.)) %>%
mutate(slope = summary(mod)$coeff[2]) %>%
mutate(R2 = summary(mod)$r.squared)%>%
mutate(P=anova(mod)$'Pr(>F)'[1])%>%
mutate(intcp= summary(mod)$coeff[1])%>%
dplyr::select(-mod)essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableLAR.ana<- ObsH%>%
group_by(Collection,ExperimentID) %>%
do(mod = lm(Observed~Interval,data=.)) %>%
mutate(slope = summary(mod)$coeff[2]) %>%
mutate(R2 = summary(mod)$r.squared)%>%
mutate(P=anova(mod)$'Pr(>F)'[1])%>%
mutate(intcp= summary(mod)$coeff[1])%>%
dplyr::select(-mod)library(plyr)-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
hereLAR.NEW<-LAR2%>%
dplyr::filter(Rotation.x!="6"|Collection!="1997_2001")%>%
mutate(NAR=slope)%>%
dplyr::filter(Collection!="2014_2019")%>%
dplyr::filter(Collection!="2010_2012")%>%
dplyr::select(Name, Collection, GrowthRotation, NAR,Tmean,Ppm,ExperimentID,ID)
#dplyr::filter(Collection==C("2000_2002","1997_2001","2014_2018","2002_2004"))
LAR.NEW$Collection [1] 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018
[11] 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018
[21] 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001
[31] 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 2002_2004
[41] 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004
[51] 2002_2004 2002_2004 2002_2004 2002_2004 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002
[61] 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002
[71] 2000_2002
Levels: 1997_2001 2000_2002 2002_2004 2010_2012 2014_2018lm_eqn <- function(LAR.NEW){
m <- lm(NAR ~ Tmean, LAR.NEW);
eq <- substitute(italic(y) == Tmean + NAR %.% italic(Tmean)*","~~italic(R)^2~"="~r2,
list(Tmean = format(coef(m)[1], digits = 2),
NAR = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
eqns <- ddply(LAR.NEW,.(ExperimentID),lm_eqn)
#df2 <- data.frame(eq = unclass(eqns), Collection = as.numeric(names(eqns)))
a<-ggplot(LAR.NEW, aes(x=Tmean, y=NAR))+
geom_point()+theme_bw()+xlab("Mean air temperature (°C)")+
geom_text(data = eqns, aes(x = 13, y = 0.48, label = V1), size=4.7,
color = 'black', parse = TRUE)+
ylab(bquote(bold('Node appearance rate ('*node~day^-1*')')))+
scale_x_continuous(limits=c(5,20))+
facet_wrap(~ExperimentID)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"))
#ggsave("D:/R/Pictures/Node/Node apparence rate.png", width=8, height=6, dpi=500)
a
detach(package:plyr)LAR2 #dplyr::filter(Rotation!="6"|Collection!="1997_2001")
LAR2.lm=lm(slope~Tmean,data=LAR2)
summary(LAR2.lm)
Call:
lm(formula = slope ~ Tmean, data = LAR2)
Residuals:
Min 1Q Median 3Q Max
-0.142755 -0.060941 -0.007349 0.056628 0.171460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001896 0.035007 0.054 0.957
Tmean 0.018709 0.002579 7.254 3.51e-10 ***
---
Signif. codes: 0 ?**?0.001 ?*?0.01 ??0.05 ??0.1 ??1
Residual standard error: 0.0702 on 73 degrees of freedom
Multiple R-squared: 0.4189, Adjusted R-squared: 0.4109
F-statistic: 52.62 on 1 and 73 DF, p-value: 3.514e-10predict(LAR2.lm, LAR2, interval="predict") fit lwr upr
1 0.2893523 0.1480724703 0.4306321
2 0.3120969 0.1701889979 0.4540047
3 0.2619888 0.1211005062 0.4028771
4 0.1869465 0.0450782027 0.3288149
5 0.2000531 0.0585718438 0.3415344
6 0.2513464 0.1105023104 0.3921906
7 0.2765514 0.1355044768 0.4175983
8 0.3349125 0.1921023944 0.4777226
9 0.2993657 0.1578430814 0.4408884
10 0.2525481 0.1117019893 0.3933941
11 0.1935086 0.0518454158 0.3351719
12 0.2392637 0.0983960731 0.3801313
13 0.2969517 0.1554924451 0.4384110
14 0.3221620 0.1798894771 0.4644346
15 0.2352880 0.0943956278 0.3761805
16 0.1333112 -0.0110690730 0.2776915
17 0.1533606 0.0100914352 0.2966298
18 0.2095842 0.0683274321 0.3508410
19 0.2702247 0.1292606681 0.4111888
20 0.3318085 0.1891370263 0.4744800
21 0.2495323 0.1086895917 0.3903749
22 0.2923774 0.1510298770 0.4337250
23 0.3336207 0.1908688777 0.4763725
24 0.3634814 0.2191650559 0.5077977
25 0.2450865 0.1042399689 0.3859330
26 0.1535398 0.0102796064 0.2967999
27 0.2354077 0.0945161478 0.3762992
28 0.3309772 0.1883419635 0.4736124
29 0.3301017 0.1875042479 0.4726991
30 0.2995955 0.1580666437 0.4411244
31 0.1463030 0.0026657522 0.2899402
32 0.2506783 0.1098348849 0.3915217
33 0.2480100 0.1071672463 0.3888528
34 0.2729272 0.1319303996 0.4139241
35 0.3034658 0.1618283601 0.4451032
36 0.2234391 0.0824225637 0.3644557
37 0.1600318 0.0170872149 0.3029764
38 0.2139924 0.0728231885 0.3551617
39 0.2661918 0.1252694018 0.4071142
40 0.2899563 0.1486633616 0.4312493
41 0.3021530 0.1605532780 0.4437527
42 0.2606499 0.1197705610 0.4015293
43 0.1540197 0.0107836082 0.2972558
44 0.1506963 0.0072911582 0.2941014
45 0.2072025 0.0658941893 0.3485109
46 0.2717034 0.1307219139 0.4126849
47 0.3040429 0.1623886825 0.4456972
48 0.2951563 0.1537421606 0.4365704
49 0.2200926 0.0790273555 0.3611578
50 0.1848582 0.0429198350 0.3267965
51 0.1446357 0.0009078732 0.2883635
52 0.2254167 0.0844260563 0.3664073
53 0.2742309 0.1332168195 0.4152449
54 0.3343193 0.1915360585 0.4771025
55 0.2843987 0.1432193579 0.4255780
56 0.2192580 0.0781797125 0.3603363
57 0.1816873 0.0396382547 0.3237363
58 0.1367467 -0.0074288436 0.2809223
59 0.2660152 0.1250944531 0.4069360
60 0.3071611 0.1654126789 0.4489095
61 0.2631297 0.1222331118 0.4040263
62 0.1518678 0.0085229389 0.2952128
63 0.1462936 0.0026559031 0.2899313
64 0.2394569 0.0985902525 0.3803235
65 0.2851066 0.1439137000 0.4262995
66 0.2989340 0.1574229054 0.4404451
67 0.2802534 0.1391481144 0.4213587
68 0.1997933 0.0583052053 0.3412813
69 0.2834632 0.1423014254 0.4246251
70 0.2780814 0.1370112789 0.4191516
71 0.1516538 0.0082979287 0.2950096
72 0.3046530 0.1629807368 0.4463253
73 0.2631297 0.1222331118 0.4040263
74 0.2977827 0.1563019543 0.4392634
75 0.2422188 0.1013641807 0.3830734# LARs<-LAR1%>%
# dplyr::filter(Collection=="2014_2018")
#
# LAR.lm = lm(Observed ~ TTS2, data=LARs)
# LAR.res = resid(LAR.lm)
#
#
# plot(LARs$Clock.Today, LAR.res, ylab="Residuals", xlab="Date")
# abline(0, 0) load Rotation and Growth season
library(plyr)-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
herephyll <- "D:\\R\\"
StartGrazing <- read.table(paste0(phyll, "ExperimentList.txt"),
header = TRUE)
StartGrazing1<-StartGrazing %>%
mutate(GrowthRotation= as.factor(paste0(GrowthSeason,Rotation)))
PhyPp<- merge(StartGrazing1,mergeP,by=c("Name","Collection","GrowthRotation"))
lm_eqn1 <- function(PhyPp){
m <- lm(Phyllo~Ppm , PhyPp);
eq <- substitute(italic(y) == Ppm + Phyllo %.% italic(Ppm)*","~~italic(R)^2~"="~r2,
list(Ppm = format(coef(m)[1], digits = 2),
Phyllo = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
eqs <- ddply(PhyPp,.(Trend,Stage),lm_eqn1)
#df2 <- data.frame(eq = unclass(eqns), Collection = as.numeric(names(eqns)))
#ylab(bquote(bold('LEAR ('*m^2~m^-2~'°Cd'^-1*')')))
PhyPpmergePPhyPp%>%
#dplyr::filter(Rotation!="1")%>%
#dplyr::filter(Rotation!="7")%>%
#dplyr::filter(Name!="Iversen_8Waterirr"|GrowthSeason!="3")%>%
#dplyr::filter(Stage!="Seedling")%>%
dplyr::filter(PhylloTb=="PhylloBr1")%>%
dplyr::filter(Collection!="2010_2012")%>%
ggplot(aes(x=Ppm, y=Phyllo,label=GrowthRotation,color=ID))+geom_text()+theme_bw()+xlab("Mean photoperiod (h)")+ylab(bquote(bold('Phyllochron ('*'°Cd'~'main node'^-1*')')))+
geom_text(data = eqs, aes(x =13, y = 78, label = V1),
color = 'black', parse = TRUE)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+
facet_grid(Trend~Stage)+
#facet_wrap(~Stage,ncol = 2)+
#geom_point(size=2)+
scale_y_continuous(breaks = seq(0, 70, by =20), limits=c(0,78))+
mytheme3+theme(legend.title = element_blank())
#ggsave("D:/R/Pictures/Node/phyll.png", width=8, height=6, dpi=500)
detach(package:plyr)load Rotation and Growth season for poster
library(plyr)-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
-------------------------------------------------------------------------------------------------------
Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
herephyll <- "D:\\R\\"
StartGrazing <- read.table(paste0(phyll, "ExperimentList.txt"),
header = TRUE)
StartGrazing1<-StartGrazing %>%
mutate(GrowthRotation= as.factor(paste0(GrowthSeason,Rotation)))
PhyPp<- merge(StartGrazing1,mergeP,by=c("Name","Collection","GrowthRotation"))
lm_eqn1 <- function(PhyPp){
m <- lm(Phyllo~Ppm , PhyPp);
eq <- substitute(italic(y) == Ppm + Phyllo %.% italic(Ppm)*","~~italic(R)^2~"="~r2,
list(Ppm = format(coef(m)[1], digits = 2),
Phyllo = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
eqs <- ddply(PhyPp,.(Trend,Stage),lm_eqn1)
#df2 <- data.frame(eq = unclass(eqns), Collection = as.numeric(names(eqns)))
#ylab(bquote(bold('LEAR ('*m^2~m^-2~'°Cd'^-1*')')))
PhyPpmergePPhyPp%>%
#dplyr::filter(Rotation!="1")%>%
#dplyr::filter(Rotation!="7")%>%
#dplyr::filter(Name!="Iversen_8Waterirr"|GrowthSeason!="3")%>%
#dplyr::filter(Stage!="Seedling")%>%
dplyr::filter(PhylloTb=="PhylloBr1")%>%
dplyr::filter(Collection!="2010_2012")%>%
ggplot(aes(x=Ppm, y=Phyllo,label=GrowthRotation,color=ID))+geom_text()+theme_bw()+xlab("Mean photoperiod (h)")+ylab(bquote(bold('Phyllochron ('*'°Cd'~'main node'^-1*')')))+
geom_text(data = eqs, aes(x =13, y = 78, label = V1),
color = 'black', parse = TRUE)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+
facet_grid(Trend~Stage)+
#facet_wrap(~Stage,ncol = 2)+
#geom_point(size=2)+
scale_y_continuous(breaks = seq(0, 70, by =20), limits=c(0,78))+
mytheme3+theme(legend.title = element_blank())
#ggsave("D:/R/Pictures/Node/phyll.png", width=8, height=6, dpi=500)
detach(package:plyr)PhyPpD<-PhyPp%>%
dplyr::filter(Stage=="Regrowth")%>%
dplyr::filter(Trend=="Dec")
mod<-lm(PhyPpD$Phyllo~PhyPpD$Ppm)
summary(mod)
Call:
lm(formula = PhyPpD$Phyllo ~ PhyPpD$Ppm)
Residuals:
Min 1Q Median 3Q Max
-35.075 -8.259 0.246 7.268 46.956
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.4449 3.3782 14.636 < 2e-16 ***
PhyPpD$Ppm -0.9483 0.2477 -3.828 0.000142 ***
---
Signif. codes: 0 ?**?0.001 ?*?0.01 ??0.05 ??0.1 ??1
Residual standard error: 13.4 on 643 degrees of freedom
Multiple R-squared: 0.02229, Adjusted R-squared: 0.02077
F-statistic: 14.66 on 1 and 643 DF, p-value: 0.0001416PhyPpI<-PhyPp%>%
dplyr::filter(Stage=="Regrowth")%>%
dplyr::filter(Trend!="Dec")
mod<-lm(PhyPpI$Phyllo~PhyPpI$Ppm)
summary(mod)
Call:
lm(formula = PhyPpI$Phyllo ~ PhyPpI$Ppm)
Residuals:
Min 1Q Median 3Q Max
-24.205 -9.945 -0.083 7.226 44.098
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.8304 4.5053 5.067 6.15e-07 ***
PhyPpI$Ppm 0.2081 0.3201 0.650 0.516
---
Signif. codes: 0 ?**?0.001 ?*?0.01 ??0.05 ??0.1 ??1
Residual standard error: 12.51 on 403 degrees of freedom
Multiple R-squared: 0.001048, Adjusted R-squared: -0.001431
F-statistic: 0.4228 on 1 and 403 DF, p-value: 0.5159Define stats function
- Using Gauch et al. 2003 (Model evaluation by comparison of model-based predictions and measured values. Agron. J. 95, 1442-1446)
Test stats functions used
s <- c(4231.972,3935.604,3779.652,3627.687,3363.499,3230.566,2868.114,2868.827)
m <- c(4987.66,5636.09,4754.06,4114.53,4141.72,3704.06,5142.19,4762.03)
x <- gauchStats(s,m)
tempDf <- data.frame(statName=c("SB","NU","LC","r_MSD","R2"), statValue=x)
# kable(tempDf, digits= 2)
tempDf2 <- data.frame(Predicted=s, Observed=m)
x <- tempDf2 %>%
summarise(
n = n(),
r2 = gauchStats(Predicted,Observed)[5],
# rmse = round(rmse(Predicted,Observed),0),
r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
nse = round(NSE(Predicted,Observed),1),
sb = gauchStats(Predicted,Observed)[1],
nu = gauchStats(Predicted,Observed)[2],
lc = gauchStats(Predicted,Observed)[3]
) %>%
t()
df <- data.frame(stat = row.names(x),statvalue = x[,1])
df %>%
kable(format = "markdown")| stat | statvalue | |
|---|---|---|
| n | n | 8.0 |
| r2 | r2 | 7.1 |
| r_rmse | r_rmse | 28.7 |
| nse | nse | -4.1 |
| sb | sb | 76.5 |
| nu | nu | 5.2 |
| lc | lc | 18.2 |
Load simulated database
create function to read data (Justin’s script)
load address of db
set table to be enquierd
load table into an object
make it a dataframe
change date to corerct format
explore the df
db.address <- "D:\\APSIMX2\\Prototypes\\Lucerne\\LucerneValidation.db"
tableName<-"Report"
DbTable <- GetApsimNGTable(db.address,tableName)
df <- as.data.frame(DbTable)
df$Clock.Today <- ymd_hms(df$Clock.Today)
str(df)'data.frame': 62472 obs. of 162 variables:
$ CheckpointID : int 1 1 1 1 1 1 1 1 1 1 ...
$ SimulationID : int 54 54 54 54 54 54 54 54 54 54 ...
$ Water : chr NA NA NA NA ...
$ Zone : chr "paddock" "paddock" "paddock" "paddock" ...
$ Clock.Today : POSIXct, format: "1994-08-05 12:00:00" "1994-08-06 12:00:00" "1994-08-07 12:00:00" ...
$ Lucerne.Root.NSupply.Fixation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Reallocation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Retranslocation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Total : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Uptake : num 0 0 0 0 0 0 0 0 0 0 ...
$ Soil.SoilWater.Eo : num 3.13 3.13 3.29 2.98 2.91 ...
$ Soil.SoilWater.Es : num 3.13 2.48 3.29 2.88 1.58 ...
$ Lucerne.Grain.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Shell.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ StemWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Stem.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Grain.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Shell.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ LeafWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.TapRoot.StructuralFraction.FixedValue : num 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 ...
$ Lucerne.TapRoot.Live.StructuralWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ StructuralWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.TapRoot.Live.StorageWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ StorageWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ RootWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ RootTotal : num 0 0 0 0 0 0 0 0 0 0 ...
$ TapRoot : num 0 0 0 0 0 0 0 0 0 0 ...
$ lucerne.TapRoot.Senesced.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.TapRoot.MaintenanceRespiration : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.Live.NConc : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Live.NConc : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.WaterUptake : num 0 0 0 0 0 0 0 0 0 0 ...
$ ET : num 3.13 2.48 3.29 2.88 1.58 ...
$ Lucerne.Leaf.WaterDemand : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Depth : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.MaintenanceRespiration : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.CoverTotal : num 0 0 0 0 0 0 0 0 0 0 ...
$ NodeNumber : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.CoverDead : num 0 0 0 0 0 0 0 0 0 0 ...
$ LAI : num 0 0 0 0 0 0 0 0 0 0 ...
$ Height : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. : num 0 0 0 0 0 0 0 0 0 0 ...
$ SWmm.1. : num 43 40.8 37.7 35.2 33.9 ...
$ SWmm.2. : num 92.2 92.3 92.4 92.4 92.4 ...
$ SWmm.3. : num 105 104 104 103 103 ...
$ SWmm.4. : num 103 103 103 103 102 ...
$ SWmm.5. : num 96.1 96.2 96.4 96.5 96.6 ...
$ SWmm.6. : num 92 92.1 92.2 92.2 92.3 ...
$ SWmm.7. : num 90 90.1 90.1 90.1 90.2 ...
$ SWmm.8. : num 90 90 90 90 90 ...
$ SWmm.9. : num 90 90 90 90 90 ...
$ SWmm.10. : num 88 88 88 88 88 ...
$ SWmm.11. : num 84 84 84 84.1 84.1 ...
$ SWmm.12. : num 84 84 84 84 84 ...
$ SWmm.13. : num 86 86 86 86 86 ...
$ SWmm.14. : num 88 88 87.9 87.9 87.9 ...
$ SWmm.15. : num 84 84 84 84.1 84.1 ...
$ SWmm.16. : num 84 84 84 84 84 ...
$ SWmm.17. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.18. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.19. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.20. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.21. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.22. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.23. : num NA NA NA NA NA NA NA NA NA NA ...
$ Lucerne.Leaf.Fw : num 1 1 1 1 1 1 1 1 1 1 ...
$ Lucerne.Leaf.Fn : num 1 1 1 1 1 1 1 1 1 1 ...
$ Lucerne.Phenology.CurrentPhaseName : chr "Germinating" "Germinating" "Germinating" "Germinating" ...
$ Lucerne.Phenology.CurrentStageName : chr "" "" "" "" ...
$ Lucerne.Phenology.Stage : num 1 1 1 1 1 1 1 1 1 1 ...
$ Lucerne.Phenology.DaysAfterCutting.Value.. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Phenology.FloweringDaysAfterCutting.Value..: num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Pod.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Pod.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ shootbiomass : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.LAIFunction.Value.. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.1. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.2. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.3. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.4. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.5. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.6. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.7. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.8. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.9. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.10. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.11. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.12. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.13. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.14. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.15. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.16. : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.LengthDensity.17. : num NA NA NA NA NA NA NA NA NA NA ...
$ Lucerne.Root.LengthDensity.18. : num NA NA NA NA NA NA NA NA NA NA ...
$ Lucerne.Root.LengthDensity.19. : num NA NA NA NA NA NA NA NA NA NA ...
$ Lucerne.Root.LengthDensity.20. : num NA NA NA NA NA NA NA NA NA NA ...
$ Lucerne.Root.LengthDensity.21. : num NA NA NA NA NA NA NA NA NA NA ...
[list output truncated]summary(df) CheckpointID SimulationID Water Zone Clock.Today
Min. :1 Min. : 1.00 Length:62472 Length:62472 Min. :1979-01-01 12:00:00
1st Qu.:1 1st Qu.:19.00 Class :character Class :character 1st Qu.:1999-03-31 12:00:00
Median :1 Median :45.00 Mode :character Mode :character Median :2001-12-04 12:00:00
Mean :1 Mean :40.89 Mean :2004-04-18 16:01:43
3rd Qu.:1 3rd Qu.:60.00 3rd Qu.:2012-02-19 12:00:00
Max. :1 Max. :73.00 Max. :2018-04-21 12:00:00
Lucerne.Root.NSupply.Fixation Lucerne.Root.NSupply.Reallocation Lucerne.Root.NSupply.Retranslocation
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :0 Max. :0 Max. :0
Lucerne.Root.NSupply.Total Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo Soil.SoilWater.Es
Min. :0 Min. :0 Min. : 0.05421 Min. :0.0000
1st Qu.:0 1st Qu.:0 1st Qu.: 1.48457 1st Qu.:0.3008
Median :0 Median :0 Median : 2.78704 Median :0.5244
Mean :0 Mean :0 Mean : 3.14887 Mean :0.7047
3rd Qu.:0 3rd Qu.:0 3rd Qu.: 4.58214 3rd Qu.:0.8846
Max. :0 Max. :0 Max. :12.36484 Max. :8.0551
Lucerne.Grain.Live.Wt Lucerne.Shell.Live.Wt StemWt Lucerne.Stem.Live.Wt
Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 66.09 1st Qu.: 6.609
Median : 0.000 Median : 0.000 Median : 354.34 Median : 35.434
Mean : 1.885 Mean : 1.885 Mean : 1918.69 Mean : 191.869
3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 1537.66 3rd Qu.: 153.766
Max. :91.375 Max. :91.375 Max. :33189.67 Max. :3318.967
Lucerne.Grain.Live.N Lucerne.Shell.Live.N LeafWt
Min. :0.00000 Min. :0.00000 Min. : 0.0
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 133.3
Median :0.00000 Median :0.00000 Median : 569.4
Mean :0.05656 Mean :0.05656 Mean : 957.4
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1470.5
Max. :2.74126 Max. :2.74126 Max. :4524.6
Lucerne.TapRoot.StructuralFraction.FixedValue Lucerne.TapRoot.Live.StructuralWt StructuralWt
Min. :0.4 Min. : 0.0 Min. : 0
1st Qu.:0.4 1st Qu.:247.0 1st Qu.:2470
Median :0.4 Median :250.0 Median :2500
Mean :0.4 Mean :219.6 Mean :2196
3rd Qu.:0.4 3rd Qu.:250.0 3rd Qu.:2500
Max. :0.4 Max. :250.0 Max. :2500
Lucerne.TapRoot.Live.StorageWt StorageWt RootWt RootTotal TapRoot
Min. : 0.0 Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 55.4 1st Qu.: 554 1st Qu.:3017 1st Qu.:3017 1st Qu.:3017
Median :136.5 Median :1365 Median :3862 Median :3862 Median :3862
Mean :171.2 Mean :1712 Mean :3908 Mean :3908 Mean :3908
3rd Qu.:256.0 3rd Qu.:2560 3rd Qu.:5060 3rd Qu.:5060 3rd Qu.:5060
Max. :500.0 Max. :5000 Max. :7500 Max. :7500 Max. :7500
lucerne.TapRoot.Senesced.Wt Lucerne.TapRoot.MaintenanceRespiration Lucerne.Leaf.Live.N
Min. :0 Min. : 0.000 Min. :0.0000000
1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000000
Median :0 Median : 1.759 Median :0.0000000
Mean :0 Mean : 3.140 Mean :0.0001189
3rd Qu.:0 3rd Qu.: 4.677 3rd Qu.:0.0000020
Max. :0 Max. :42.515 Max. :0.0008000
Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc Lucerne.Root.WaterUptake
Min. : 0.000 Min. :0.00e+00 Min. :0.000000 Min. :0.0000
1st Qu.: 1.584 1st Qu.:0.00e+00 1st Qu.:0.009984 1st Qu.:0.1610
Median : 3.644 Median :0.00e+00 Median :0.009996 Median :0.6496
Mean : 4.862 Mean :3.98e-04 Mean :0.009805 Mean :1.1773
3rd Qu.: 6.922 3rd Qu.:3.20e-08 3rd Qu.:0.010000 3rd Qu.:1.7908
Max. :23.482 Max. :8.00e-03 Max. :0.010000 Max. :8.7263
ET Lucerne.Leaf.WaterDemand Lucerne.Root.Depth Lucerne.Root.MaintenanceRespiration
Min. : 0.0000 Min. : 0.0000 Min. : 0 Min. :0
1st Qu.: 0.6744 1st Qu.: 0.2734 1st Qu.:1500 1st Qu.:0
Median : 1.4025 Median : 1.2037 Median :2300 Median :0
Mean : 1.8820 Mean : 1.9003 Mean :1983 Mean :0
3rd Qu.: 2.7257 3rd Qu.: 2.9877 3rd Qu.:2300 3rd Qu.:0
Max. :10.4193 Max. :11.2049 Max. :4300 Max. :0
Lucerne.Leaf.CoverTotal NodeNumber Lucerne.Leaf.CoverDead LAI Height
Min. :0.0000 Min. : 0.000 Min. :0 Min. : 0.0000 Min. : 0.0
1st Qu.:0.4801 1st Qu.: 3.151 1st Qu.:0 1st Qu.: 0.9628 1st Qu.: 61.9
Median :0.8614 Median : 7.215 Median :0 Median : 2.7262 Median : 190.2
Mean :0.6993 Mean : 9.982 Mean :0 Mean : 4.2934 Mean : 314.0
3rd Qu.:0.9866 3rd Qu.:13.356 3rd Qu.:0 3rd Qu.: 5.8793 3rd Qu.: 475.0
Max. :1.0000 Max. :53.761 Max. :0 Max. :28.2734 Max. :1475.0
Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. SWmm.1. SWmm.2. SWmm.3.
Min. : 0.000 Min. : 1.00 Min. : 2.00 Min. : 5.00
1st Qu.: 2.181 1st Qu.:19.68 1st Qu.: 21.18 1st Qu.: 21.47
Median : 5.313 Median :29.56 Median : 31.47 Median : 28.31
Mean : 8.038 Mean :29.00 Mean : 30.76 Mean : 34.11
3rd Qu.:12.791 3rd Qu.:35.10 3rd Qu.: 35.19 3rd Qu.: 34.43
Max. :33.789 Max. :85.25 Max. :110.80 Max. :145.02
SWmm.4. SWmm.5. SWmm.6. SWmm.7. SWmm.8.
Min. : 7.79 Min. : 7.338 Min. : 7.277 Min. : 7.217 Min. : 7.33
1st Qu.: 21.86 1st Qu.: 23.172 1st Qu.: 23.672 1st Qu.: 20.837 1st Qu.: 18.34
Median : 28.61 Median : 27.232 Median : 29.349 Median : 29.003 Median : 29.43
Mean : 38.52 Mean : 40.316 Mean : 41.536 Mean : 42.980 Mean : 41.40
3rd Qu.: 47.90 3rd Qu.: 51.897 3rd Qu.: 54.985 3rd Qu.: 59.509 3rd Qu.: 44.00
Max. :180.19 Max. :211.030 Max. :200.200 Max. :141.793 Max. :169.12
SWmm.9. SWmm.10. SWmm.11. SWmm.12. SWmm.13. SWmm.14.
Min. : 7.22 Min. : 7.301 Min. : 7.59 Min. : 8.58 Min. : 8.68 Min. : 9.10
1st Qu.: 16.40 1st Qu.: 17.351 1st Qu.: 16.03 1st Qu.: 16.33 1st Qu.:16.25 1st Qu.:16.37
Median : 29.21 Median : 29.853 Median : 28.74 Median : 27.16 Median :26.96 Median :26.97
Mean : 40.52 Mean : 41.058 Mean : 35.93 Mean : 28.88 Mean :27.59 Mean :28.06
3rd Qu.: 44.00 3rd Qu.: 42.105 3rd Qu.: 34.08 3rd Qu.: 33.95 3rd Qu.:32.09 3rd Qu.:33.00
Max. :165.41 Max. :163.243 Max. :142.27 Max. :120.00 Max. :86.00 Max. :87.98
SWmm.15. SWmm.16. SWmm.17. SWmm.18. SWmm.19. SWmm.20.
Min. :10.00 Min. :10.23 Min. : 9.83 Min. : 9.78 Min. : 9.72 Min. : 9.99
1st Qu.:16.55 1st Qu.:20.07 1st Qu.:25.00 1st Qu.:25.00 1st Qu.:25.00 1st Qu.:25.00
Median :28.20 Median :27.89 Median :27.70 Median :28.49 Median :28.77 Median :29.44
Mean :28.80 Mean :30.10 Mean :29.06 Mean :29.41 Mean :29.68 Mean :29.93
3rd Qu.:33.98 3rd Qu.:35.48 3rd Qu.:34.94 3rd Qu.:35.42 3rd Qu.:35.63 3rd Qu.:35.66
Max. :85.52 Max. :85.76 Max. :59.60 Max. :59.41 Max. :63.99 Max. :66.50
SWmm.21. SWmm.22. SWmm.23. Lucerne.Leaf.Fw Lucerne.Leaf.Fn
Min. :10.18 Min. :10.69 Min. :15.00 Min. :0.0000 Min. :0.0000000
1st Qu.:23.01 1st Qu.:16.07 1st Qu.:19.30 1st Qu.:0.8677 1st Qu.:0.0000000
Median :29.43 Median :29.25 Median :30.82 Median :1.0000 Median :0.0000000
Mean :29.95 Mean :28.33 Mean :29.98 Mean :0.8202 Mean :0.0675837
3rd Qu.:35.69 3rd Qu.:36.34 3rd Qu.:36.37 3rd Qu.:1.0000 3rd Qu.:0.0000112
Max. :71.95 Max. :64.65 Max. :60.00 Max. :1.0000 Max. :1.0000000
Lucerne.Phenology.CurrentPhaseName Lucerne.Phenology.CurrentStageName Lucerne.Phenology.Stage
Length:62472 Length:62472 Min. :1.000
Class :character Class :character 1st Qu.:4.314
Mode :character Mode :character Median :4.829
Mean :4.957
3rd Qu.:5.590
Max. :9.000
Lucerne.Phenology.DaysAfterCutting.Value.. Lucerne.Phenology.FloweringDaysAfterCutting.Value..
Min. : 0.00 Min. : 0.00
1st Qu.: 13.00 1st Qu.: 33.00
Median : 30.00 Median : 44.00
Mean : 33.22 Mean : 44.85
3rd Qu.: 46.00 3rd Qu.: 54.00
Max. :163.00 Max. :163.00
Lucerne.Pod.Wt Lucerne.Pod.N shootbiomass Lucerne.Leaf.LAIFunction.Value..
Min. : 0.000 Min. :0.0000 Min. : 0.0 Min. : 0.0000
1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 213.7 1st Qu.: 0.9628
Median : 0.000 Median :0.0000 Median : 938.0 Median : 2.7262
Mean : 3.771 Mean :0.1131 Mean : 2913.8 Mean : 4.2934
3rd Qu.: 0.000 3rd Qu.:0.0000 3rd Qu.: 3021.1 3rd Qu.: 5.8793
Max. :182.751 Max. :5.4825 Max. :38562.5 Max. :28.2734
Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2. Lucerne.Root.LengthDensity.3.
Min. :0.000000 Min. :0.000000 Min. :0.000000
1st Qu.:0.009406 1st Qu.:0.004636 1st Qu.:0.003086
Median :0.025600 Median :0.011073 Median :0.007748
Mean :0.034531 Mean :0.024192 Mean :0.015851
3rd Qu.:0.041451 3rd Qu.:0.028448 3rd Qu.:0.017398
Max. :0.211993 Max. :0.194046 Max. :0.183551
Lucerne.Root.LengthDensity.4. Lucerne.Root.LengthDensity.5. Lucerne.Root.LengthDensity.6.
Min. :0.000000 Min. :0.000000 Min. :0.0000
1st Qu.:0.002929 1st Qu.:0.002833 1st Qu.:0.0025
Median :0.005947 Median :0.004821 Median :0.0044
Mean :0.008581 Mean :0.007522 Mean :0.0071
3rd Qu.:0.012943 3rd Qu.:0.010879 3rd Qu.:0.0105
Max. :0.054335 Max. :0.039362 Max. :0.0423
Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8. Lucerne.Root.LengthDensity.9.
Min. :0.0000 Min. :0.000 Min. :0.000
1st Qu.:0.0027 1st Qu.:0.002 1st Qu.:0.002
Median :0.0043 Median :0.005 Median :0.005
Mean :0.0070 Mean :0.007 Mean :0.006
3rd Qu.:0.0102 3rd Qu.:0.010 3rd Qu.:0.008
Max. :0.0434 Max. :0.036 Max. :0.029
Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11. Lucerne.Root.LengthDensity.12.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.002 1st Qu.:0.002
Median :0.004 Median :0.004 Median :0.004
Mean :0.005 Mean :0.005 Mean :0.005
3rd Qu.:0.008 3rd Qu.:0.006 3rd Qu.:0.006
Max. :0.025 Max. :0.022 Max. :0.018
Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14. Lucerne.Root.LengthDensity.15.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.002 1st Qu.:0.002 1st Qu.:0.001
Median :0.004 Median :0.004 Median :0.004
Mean :0.005 Mean :0.004 Mean :0.004
3rd Qu.:0.006 3rd Qu.:0.006 3rd Qu.:0.006
Max. :0.031 Max. :0.030 Max. :0.032
Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17. Lucerne.Root.LengthDensity.18.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.001 1st Qu.:0.000
Median :0.004 Median :0.002 Median :0.002
Mean :0.004 Mean :0.003 Mean :0.002
3rd Qu.:0.006 3rd Qu.:0.004 3rd Qu.:0.003
Max. :0.014 Max. :0.011 Max. :0.009
Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20. Lucerne.Root.LengthDensity.21.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.002 Median :0.002 Median :0.001
Mean :0.002 Mean :0.003 Mean :0.002
3rd Qu.:0.003 3rd Qu.:0.003 3rd Qu.:0.003
Max. :0.015 Max. :0.043 Max. :0.009
Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23. Soil.SoilWater.WaterTable
Min. :0.000 Min. :0.000 Min. : 0.784
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1500.000
Median :0.001 Median :0.003 Median :2300.000
Mean :0.003 Mean :0.004 Mean :2097.406
3rd Qu.:0.005 3rd Qu.:0.005 3rd Qu.:2300.000
Max. :0.020 Max. :0.020 Max. :5000.000
Lucerne.AboveGround.Wt Lucerne.AboveGround.N SowingDate Defoliation FD
Min. : 0.00 Min. : 0.0000 Length:62472 Length:62472 Length:62472
1st Qu.: 21.37 1st Qu.: 0.1997 Class :character Class :character Class :character
Median : 93.80 Median : 1.0671 Mode :character Mode :character Mode :character
Mean : 291.38 Mean : 5.8601
3rd Qu.: 302.11 3rd Qu.: 4.6333
Max. :3856.25 Max. :101.6753
SD1 Soil.OutputLayers.SWmm.1. Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3.
Length:62472 Min. : 3.00 Min. : 21.00 Min. : 42.09
Class :character 1st Qu.: 15.35 1st Qu.: 28.20 1st Qu.: 54.80
Mode :character Median : 22.77 Median : 61.38 Median : 70.68
Mean : 25.88 Mean : 74.05 Mean : 86.18
3rd Qu.: 36.41 3rd Qu.:112.22 3rd Qu.:116.00
Max. :105.99 Max. :220.72 Max. :213.12
Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5. Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7.
Min. : 0.00 Min. : 51.33 Min. : 54.78 Min. : 57.97
1st Qu.: 50.99 1st Qu.: 86.04 1st Qu.: 74.01 1st Qu.: 90.00
Median : 80.00 Median :118.71 Median : 84.80 Median : 90.74
Mean : 85.55 Mean :113.69 Mean :102.29 Mean :107.72
3rd Qu.:116.01 3rd Qu.:143.70 3rd Qu.:140.00 3rd Qu.:140.13
Max. :206.90 Max. :259.00 Max. :177.09 Max. :172.03
Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1. Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3.
Min. : 60.88 Min. :0.03 Min. :0.05 Min. :0.05
1st Qu.: 88.00 1st Qu.:0.08 1st Qu.:0.09 1st Qu.:0.16
Median : 90.19 Median :0.18 Median :0.22 Median :0.23
Mean :136.96 Mean :0.20 Mean :0.23 Mean :0.24
3rd Qu.:220.11 3rd Qu.:0.28 3rd Qu.:0.31 3rd Qu.:0.29
Max. :256.84 Max. :0.51 Max. :0.55 Max. :0.53
Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5. Soil.OutputLayers.SW.6. Soil.OutputLayers.SW.7.
Min. :0.00 Min. :0.22 Min. :0.19 Min. :0.22
1st Qu.:0.20 1st Qu.:0.23 1st Qu.:0.19 1st Qu.:0.22
Median :0.25 Median :0.29 Median :0.28 Median :0.29
Mean :0.25 Mean :0.29 Mean :0.28 Mean :0.30
3rd Qu.:0.29 3rd Qu.:0.33 3rd Qu.:0.35 3rd Qu.:0.35
Max. :0.52 Max. :0.52 Max. :0.44 Max. :0.43
Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9. Soil.OutputLayers.SWmm.10. Soil.OutputLayers.SWmm.11.
Min. :0.22 Min. :63.00 Min. :64.01 Min. :64.00
1st Qu.:0.22 1st Qu.:63.34 1st Qu.:64.33 1st Qu.:64.00
Median :0.31 Median :63.70 Median :64.77 Median :64.00
Mean :0.30 Mean :63.66 Mean :64.87 Mean :64.35
3rd Qu.:0.37 3rd Qu.:63.99 3rd Qu.:65.40 3rd Qu.:64.38
Max. :0.43 Max. :64.21 Max. :66.00 Max. :66.00
Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13. Soil.OutputLayers.SWmm.14.
Min. :60.10 Min. :58.00 Min. :54.00
1st Qu.:60.15 1st Qu.:58.00 1st Qu.:54.01
Median :60.29 Median :58.00 Median :54.20
Mean :60.69 Mean :58.00 Mean :54.23
3rd Qu.:61.13 3rd Qu.:58.00 3rd Qu.:54.41
Max. :62.01 Max. :58.04 Max. :54.65
Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17.
Min. :54.00 Min. :54.00 Min. :54.50
1st Qu.:54.01 1st Qu.:54.34 1st Qu.:54.57
Median :54.06 Median :54.43 Median :54.67
Mean :54.05 Mean :54.40 Mean :55.06
3rd Qu.:54.08 3rd Qu.:54.49 3rd Qu.:55.89
Max. :54.12 Max. :54.52 Max. :56.28
Soil.OutputLayers.SWmm.18. Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20.
Min. :58.01 Min. :60.84 Min. :61.20
1st Qu.:58.81 1st Qu.:61.69 1st Qu.:62.48
Median :59.10 Median :62.50 Median :63.82
Mean :59.03 Mean :62.46 Mean :63.96
3rd Qu.:59.33 3rd Qu.:63.30 3rd Qu.:65.86
Max. :59.41 Max. :63.99 Max. :66.50
Soil.OutputLayers.SWmm.21. Soil.OutputLayers.SWmm.22. Soil.OutputLayers.SWmm.23.
Min. :58.82 Min. :51.15 Min. :0
1st Qu.:60.49 1st Qu.:51.55 1st Qu.:0
Median :63.13 Median :53.14 Median :0
Mean :63.65 Mean :56.05 Mean :0
3rd Qu.:66.49 3rd Qu.:61.78 3rd Qu.:0
Max. :71.95 Max. :64.65 Max. :0
Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10. Soil.OutputLayers.SW.11. Soil.OutputLayers.SW.12.
Min. :0.32 Min. :0.32 Min. :0.32 Min. :0.30
1st Qu.:0.32 1st Qu.:0.32 1st Qu.:0.32 1st Qu.:0.30
Median :0.32 Median :0.32 Median :0.32 Median :0.30
Mean :0.32 Mean :0.32 Mean :0.32 Mean :0.30
3rd Qu.:0.32 3rd Qu.:0.33 3rd Qu.:0.32 3rd Qu.:0.31
Max. :0.32 Max. :0.33 Max. :0.33 Max. :0.31
Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14. Soil.OutputLayers.SW.15. Soil.OutputLayers.SW.16.
Min. :0.29 Min. :0.27 Min. :0.27 Min. :0.27
1st Qu.:0.29 1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.27
Median :0.29 Median :0.27 Median :0.27 Median :0.27
Mean :0.29 Mean :0.27 Mean :0.27 Mean :0.27
3rd Qu.:0.29 3rd Qu.:0.27 3rd Qu.:0.27 3rd Qu.:0.27
Max. :0.29 Max. :0.27 Max. :0.27 Max. :0.27
Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18. Soil.OutputLayers.SW.19. Soil.OutputLayers.SW.20.
Min. :0.27 Min. :0.29 Min. :0.30 Min. :0.31
1st Qu.:0.27 1st Qu.:0.29 1st Qu.:0.31 1st Qu.:0.31
Median :0.27 Median :0.30 Median :0.31 Median :0.32
Mean :0.28 Mean :0.30 Mean :0.31 Mean :0.32
3rd Qu.:0.28 3rd Qu.:0.30 3rd Qu.:0.32 3rd Qu.:0.33
Max. :0.28 Max. :0.30 Max. :0.32 Max. :0.33
Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22. Soil.OutputLayers.SW.23. SWmm.24.
Min. :0.29 Min. :0.26 Min. :0 Min. :58.00
1st Qu.:0.30 1st Qu.:0.26 1st Qu.:0 1st Qu.:58.63
Median :0.32 Median :0.27 Median :0 Median :60.00
Mean :0.32 Mean :0.28 Mean :0 Mean :59.37
3rd Qu.:0.33 3rd Qu.:0.31 3rd Qu.:0 3rd Qu.:60.00
Max. :0.36 Max. :0.32 Max. :0 Max. :60.00
SWmm.25. SWmm.26. SWmm.27. Lucerne.Root.LengthDensity.24.
Min. :58.00 Min. :58.00 Min. :58.00 Min. :0
1st Qu.:60.00 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:0
Median :60.00 Median :60.00 Median :60.00 Median :0
Mean :59.76 Mean :59.75 Mean :59.75 Mean :0
3rd Qu.:60.00 3rd Qu.:60.00 3rd Qu.:60.00 3rd Qu.:0
Max. :60.00 Max. :60.00 Max. :60.00 Max. :0
Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26. Lucerne.Root.LengthDensity.27.
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :0 Max. :0 Max. :0
[ reached getOption("max.print") -- omitted 1 row ]head(df) # simulation resultsget sim names (different table)
merge names
remove unecessary variables
simNameDf <- as.data.frame (GetApsimNGTable(db.address,"_Simulations"))
myDb <- merge(df, simNameDf, by.x= c("SimulationID"), by.y= c("ID"))
#str(myDb)
head(myDb)summary(myDb) SimulationID CheckpointID Water Zone Clock.Today
Min. : 1.00 Min. :1 Length:62472 Length:62472 Min. :1979-01-01 12:00:00
1st Qu.:19.00 1st Qu.:1 Class :character Class :character 1st Qu.:1999-03-31 12:00:00
Median :45.00 Median :1 Mode :character Mode :character Median :2001-12-04 12:00:00
Mean :40.89 Mean :1 Mean :2004-04-18 16:01:43
3rd Qu.:60.00 3rd Qu.:1 3rd Qu.:2012-02-19 12:00:00
Max. :73.00 Max. :1 Max. :2018-04-21 12:00:00
Lucerne.Root.NSupply.Fixation Lucerne.Root.NSupply.Reallocation Lucerne.Root.NSupply.Retranslocation
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :0 Max. :0 Max. :0
Lucerne.Root.NSupply.Total Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo Soil.SoilWater.Es
Min. :0 Min. :0 Min. : 0.05421 Min. :0.0000
1st Qu.:0 1st Qu.:0 1st Qu.: 1.48457 1st Qu.:0.3008
Median :0 Median :0 Median : 2.78704 Median :0.5244
Mean :0 Mean :0 Mean : 3.14887 Mean :0.7047
3rd Qu.:0 3rd Qu.:0 3rd Qu.: 4.58214 3rd Qu.:0.8846
Max. :0 Max. :0 Max. :12.36484 Max. :8.0551
Lucerne.Grain.Live.Wt Lucerne.Shell.Live.Wt StemWt Lucerne.Stem.Live.Wt
Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 66.09 1st Qu.: 6.609
Median : 0.000 Median : 0.000 Median : 354.34 Median : 35.434
Mean : 1.885 Mean : 1.885 Mean : 1918.69 Mean : 191.869
3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 1537.66 3rd Qu.: 153.766
Max. :91.375 Max. :91.375 Max. :33189.67 Max. :3318.967
Lucerne.Grain.Live.N Lucerne.Shell.Live.N LeafWt
Min. :0.00000 Min. :0.00000 Min. : 0.0
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 133.3
Median :0.00000 Median :0.00000 Median : 569.4
Mean :0.05656 Mean :0.05656 Mean : 957.4
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1470.5
Max. :2.74126 Max. :2.74126 Max. :4524.6
Lucerne.TapRoot.StructuralFraction.FixedValue Lucerne.TapRoot.Live.StructuralWt StructuralWt
Min. :0.4 Min. : 0.0 Min. : 0
1st Qu.:0.4 1st Qu.:247.0 1st Qu.:2470
Median :0.4 Median :250.0 Median :2500
Mean :0.4 Mean :219.6 Mean :2196
3rd Qu.:0.4 3rd Qu.:250.0 3rd Qu.:2500
Max. :0.4 Max. :250.0 Max. :2500
Lucerne.TapRoot.Live.StorageWt StorageWt RootWt RootTotal TapRoot
Min. : 0.0 Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 55.4 1st Qu.: 554 1st Qu.:3017 1st Qu.:3017 1st Qu.:3017
Median :136.5 Median :1365 Median :3862 Median :3862 Median :3862
Mean :171.2 Mean :1712 Mean :3908 Mean :3908 Mean :3908
3rd Qu.:256.0 3rd Qu.:2560 3rd Qu.:5060 3rd Qu.:5060 3rd Qu.:5060
Max. :500.0 Max. :5000 Max. :7500 Max. :7500 Max. :7500
lucerne.TapRoot.Senesced.Wt Lucerne.TapRoot.MaintenanceRespiration Lucerne.Leaf.Live.N
Min. :0 Min. : 0.000 Min. :0.0000000
1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000000
Median :0 Median : 1.759 Median :0.0000000
Mean :0 Mean : 3.140 Mean :0.0001189
3rd Qu.:0 3rd Qu.: 4.677 3rd Qu.:0.0000020
Max. :0 Max. :42.515 Max. :0.0008000
Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc Lucerne.Root.WaterUptake
Min. : 0.000 Min. :0.00e+00 Min. :0.000000 Min. :0.0000
1st Qu.: 1.584 1st Qu.:0.00e+00 1st Qu.:0.009984 1st Qu.:0.1610
Median : 3.644 Median :0.00e+00 Median :0.009996 Median :0.6496
Mean : 4.862 Mean :3.98e-04 Mean :0.009805 Mean :1.1773
3rd Qu.: 6.922 3rd Qu.:3.20e-08 3rd Qu.:0.010000 3rd Qu.:1.7908
Max. :23.482 Max. :8.00e-03 Max. :0.010000 Max. :8.7263
ET Lucerne.Leaf.WaterDemand Lucerne.Root.Depth Lucerne.Root.MaintenanceRespiration
Min. : 0.0000 Min. : 0.0000 Min. : 0 Min. :0
1st Qu.: 0.6744 1st Qu.: 0.2734 1st Qu.:1500 1st Qu.:0
Median : 1.4025 Median : 1.2037 Median :2300 Median :0
Mean : 1.8820 Mean : 1.9003 Mean :1983 Mean :0
3rd Qu.: 2.7257 3rd Qu.: 2.9877 3rd Qu.:2300 3rd Qu.:0
Max. :10.4193 Max. :11.2049 Max. :4300 Max. :0
Lucerne.Leaf.CoverTotal NodeNumber Lucerne.Leaf.CoverDead LAI Height
Min. :0.0000 Min. : 0.000 Min. :0 Min. : 0.0000 Min. : 0.0
1st Qu.:0.4801 1st Qu.: 3.151 1st Qu.:0 1st Qu.: 0.9628 1st Qu.: 61.9
Median :0.8614 Median : 7.215 Median :0 Median : 2.7262 Median : 190.2
Mean :0.6993 Mean : 9.982 Mean :0 Mean : 4.2934 Mean : 314.0
3rd Qu.:0.9866 3rd Qu.:13.356 3rd Qu.:0 3rd Qu.: 5.8793 3rd Qu.: 475.0
Max. :1.0000 Max. :53.761 Max. :0 Max. :28.2734 Max. :1475.0
Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. SWmm.1. SWmm.2. SWmm.3.
Min. : 0.000 Min. : 1.00 Min. : 2.00 Min. : 5.00
1st Qu.: 2.181 1st Qu.:19.68 1st Qu.: 21.18 1st Qu.: 21.47
Median : 5.313 Median :29.56 Median : 31.47 Median : 28.31
Mean : 8.038 Mean :29.00 Mean : 30.76 Mean : 34.11
3rd Qu.:12.791 3rd Qu.:35.10 3rd Qu.: 35.19 3rd Qu.: 34.43
Max. :33.789 Max. :85.25 Max. :110.80 Max. :145.02
SWmm.4. SWmm.5. SWmm.6. SWmm.7. SWmm.8.
Min. : 7.79 Min. : 7.338 Min. : 7.277 Min. : 7.217 Min. : 7.33
1st Qu.: 21.86 1st Qu.: 23.172 1st Qu.: 23.672 1st Qu.: 20.837 1st Qu.: 18.34
Median : 28.61 Median : 27.232 Median : 29.349 Median : 29.003 Median : 29.43
Mean : 38.52 Mean : 40.316 Mean : 41.536 Mean : 42.980 Mean : 41.40
3rd Qu.: 47.90 3rd Qu.: 51.897 3rd Qu.: 54.985 3rd Qu.: 59.509 3rd Qu.: 44.00
Max. :180.19 Max. :211.030 Max. :200.200 Max. :141.793 Max. :169.12
SWmm.9. SWmm.10. SWmm.11. SWmm.12. SWmm.13. SWmm.14.
Min. : 7.22 Min. : 7.301 Min. : 7.59 Min. : 8.58 Min. : 8.68 Min. : 9.10
1st Qu.: 16.40 1st Qu.: 17.351 1st Qu.: 16.03 1st Qu.: 16.33 1st Qu.:16.25 1st Qu.:16.37
Median : 29.21 Median : 29.853 Median : 28.74 Median : 27.16 Median :26.96 Median :26.97
Mean : 40.52 Mean : 41.058 Mean : 35.93 Mean : 28.88 Mean :27.59 Mean :28.06
3rd Qu.: 44.00 3rd Qu.: 42.105 3rd Qu.: 34.08 3rd Qu.: 33.95 3rd Qu.:32.09 3rd Qu.:33.00
Max. :165.41 Max. :163.243 Max. :142.27 Max. :120.00 Max. :86.00 Max. :87.98
SWmm.15. SWmm.16. SWmm.17. SWmm.18. SWmm.19. SWmm.20.
Min. :10.00 Min. :10.23 Min. : 9.83 Min. : 9.78 Min. : 9.72 Min. : 9.99
1st Qu.:16.55 1st Qu.:20.07 1st Qu.:25.00 1st Qu.:25.00 1st Qu.:25.00 1st Qu.:25.00
Median :28.20 Median :27.89 Median :27.70 Median :28.49 Median :28.77 Median :29.44
Mean :28.80 Mean :30.10 Mean :29.06 Mean :29.41 Mean :29.68 Mean :29.93
3rd Qu.:33.98 3rd Qu.:35.48 3rd Qu.:34.94 3rd Qu.:35.42 3rd Qu.:35.63 3rd Qu.:35.66
Max. :85.52 Max. :85.76 Max. :59.60 Max. :59.41 Max. :63.99 Max. :66.50
SWmm.21. SWmm.22. SWmm.23. Lucerne.Leaf.Fw Lucerne.Leaf.Fn
Min. :10.18 Min. :10.69 Min. :15.00 Min. :0.0000 Min. :0.0000000
1st Qu.:23.01 1st Qu.:16.07 1st Qu.:19.30 1st Qu.:0.8677 1st Qu.:0.0000000
Median :29.43 Median :29.25 Median :30.82 Median :1.0000 Median :0.0000000
Mean :29.95 Mean :28.33 Mean :29.98 Mean :0.8202 Mean :0.0675837
3rd Qu.:35.69 3rd Qu.:36.34 3rd Qu.:36.37 3rd Qu.:1.0000 3rd Qu.:0.0000112
Max. :71.95 Max. :64.65 Max. :60.00 Max. :1.0000 Max. :1.0000000
Lucerne.Phenology.CurrentPhaseName Lucerne.Phenology.CurrentStageName Lucerne.Phenology.Stage
Length:62472 Length:62472 Min. :1.000
Class :character Class :character 1st Qu.:4.314
Mode :character Mode :character Median :4.829
Mean :4.957
3rd Qu.:5.590
Max. :9.000
Lucerne.Phenology.DaysAfterCutting.Value.. Lucerne.Phenology.FloweringDaysAfterCutting.Value..
Min. : 0.00 Min. : 0.00
1st Qu.: 13.00 1st Qu.: 33.00
Median : 30.00 Median : 44.00
Mean : 33.22 Mean : 44.85
3rd Qu.: 46.00 3rd Qu.: 54.00
Max. :163.00 Max. :163.00
Lucerne.Pod.Wt Lucerne.Pod.N shootbiomass Lucerne.Leaf.LAIFunction.Value..
Min. : 0.000 Min. :0.0000 Min. : 0.0 Min. : 0.0000
1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 213.7 1st Qu.: 0.9628
Median : 0.000 Median :0.0000 Median : 938.0 Median : 2.7262
Mean : 3.771 Mean :0.1131 Mean : 2913.8 Mean : 4.2934
3rd Qu.: 0.000 3rd Qu.:0.0000 3rd Qu.: 3021.1 3rd Qu.: 5.8793
Max. :182.751 Max. :5.4825 Max. :38562.5 Max. :28.2734
Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2. Lucerne.Root.LengthDensity.3.
Min. :0.000000 Min. :0.000000 Min. :0.000000
1st Qu.:0.009406 1st Qu.:0.004636 1st Qu.:0.003086
Median :0.025600 Median :0.011073 Median :0.007748
Mean :0.034531 Mean :0.024192 Mean :0.015851
3rd Qu.:0.041451 3rd Qu.:0.028448 3rd Qu.:0.017398
Max. :0.211993 Max. :0.194046 Max. :0.183551
Lucerne.Root.LengthDensity.4. Lucerne.Root.LengthDensity.5. Lucerne.Root.LengthDensity.6.
Min. :0.000000 Min. :0.000000 Min. :0.0000
1st Qu.:0.002929 1st Qu.:0.002833 1st Qu.:0.0025
Median :0.005947 Median :0.004821 Median :0.0044
Mean :0.008581 Mean :0.007522 Mean :0.0071
3rd Qu.:0.012943 3rd Qu.:0.010879 3rd Qu.:0.0105
Max. :0.054335 Max. :0.039362 Max. :0.0423
Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8. Lucerne.Root.LengthDensity.9.
Min. :0.0000 Min. :0.000 Min. :0.000
1st Qu.:0.0027 1st Qu.:0.002 1st Qu.:0.002
Median :0.0043 Median :0.005 Median :0.005
Mean :0.0070 Mean :0.007 Mean :0.006
3rd Qu.:0.0102 3rd Qu.:0.010 3rd Qu.:0.008
Max. :0.0434 Max. :0.036 Max. :0.029
Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11. Lucerne.Root.LengthDensity.12.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.002 1st Qu.:0.002
Median :0.004 Median :0.004 Median :0.004
Mean :0.005 Mean :0.005 Mean :0.005
3rd Qu.:0.008 3rd Qu.:0.006 3rd Qu.:0.006
Max. :0.025 Max. :0.022 Max. :0.018
Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14. Lucerne.Root.LengthDensity.15.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.002 1st Qu.:0.002 1st Qu.:0.001
Median :0.004 Median :0.004 Median :0.004
Mean :0.005 Mean :0.004 Mean :0.004
3rd Qu.:0.006 3rd Qu.:0.006 3rd Qu.:0.006
Max. :0.031 Max. :0.030 Max. :0.032
Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17. Lucerne.Root.LengthDensity.18.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.001 1st Qu.:0.000
Median :0.004 Median :0.002 Median :0.002
Mean :0.004 Mean :0.003 Mean :0.002
3rd Qu.:0.006 3rd Qu.:0.004 3rd Qu.:0.003
Max. :0.014 Max. :0.011 Max. :0.009
Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20. Lucerne.Root.LengthDensity.21.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.002 Median :0.002 Median :0.001
Mean :0.002 Mean :0.003 Mean :0.002
3rd Qu.:0.003 3rd Qu.:0.003 3rd Qu.:0.003
Max. :0.015 Max. :0.043 Max. :0.009
Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23. Soil.SoilWater.WaterTable
Min. :0.000 Min. :0.000 Min. : 0.784
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1500.000
Median :0.001 Median :0.003 Median :2300.000
Mean :0.003 Mean :0.004 Mean :2097.406
3rd Qu.:0.005 3rd Qu.:0.005 3rd Qu.:2300.000
Max. :0.020 Max. :0.020 Max. :5000.000
Lucerne.AboveGround.Wt Lucerne.AboveGround.N SowingDate Defoliation FD
Min. : 0.00 Min. : 0.0000 Length:62472 Length:62472 Length:62472
1st Qu.: 21.37 1st Qu.: 0.1997 Class :character Class :character Class :character
Median : 93.80 Median : 1.0671 Mode :character Mode :character Mode :character
Mean : 291.38 Mean : 5.8601
3rd Qu.: 302.11 3rd Qu.: 4.6333
Max. :3856.25 Max. :101.6753
SD1 Soil.OutputLayers.SWmm.1. Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3.
Length:62472 Min. : 3.00 Min. : 21.00 Min. : 42.09
Class :character 1st Qu.: 15.35 1st Qu.: 28.20 1st Qu.: 54.80
Mode :character Median : 22.77 Median : 61.38 Median : 70.68
Mean : 25.88 Mean : 74.05 Mean : 86.18
3rd Qu.: 36.41 3rd Qu.:112.22 3rd Qu.:116.00
Max. :105.99 Max. :220.72 Max. :213.12
Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5. Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7.
Min. : 0.00 Min. : 51.33 Min. : 54.78 Min. : 57.97
1st Qu.: 50.99 1st Qu.: 86.04 1st Qu.: 74.01 1st Qu.: 90.00
Median : 80.00 Median :118.71 Median : 84.80 Median : 90.74
Mean : 85.55 Mean :113.69 Mean :102.29 Mean :107.72
3rd Qu.:116.01 3rd Qu.:143.70 3rd Qu.:140.00 3rd Qu.:140.13
Max. :206.90 Max. :259.00 Max. :177.09 Max. :172.03
Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1. Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3.
Min. : 60.88 Min. :0.03 Min. :0.05 Min. :0.05
1st Qu.: 88.00 1st Qu.:0.08 1st Qu.:0.09 1st Qu.:0.16
Median : 90.19 Median :0.18 Median :0.22 Median :0.23
Mean :136.96 Mean :0.20 Mean :0.23 Mean :0.24
3rd Qu.:220.11 3rd Qu.:0.28 3rd Qu.:0.31 3rd Qu.:0.29
Max. :256.84 Max. :0.51 Max. :0.55 Max. :0.53
Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5. Soil.OutputLayers.SW.6. Soil.OutputLayers.SW.7.
Min. :0.00 Min. :0.22 Min. :0.19 Min. :0.22
1st Qu.:0.20 1st Qu.:0.23 1st Qu.:0.19 1st Qu.:0.22
Median :0.25 Median :0.29 Median :0.28 Median :0.29
Mean :0.25 Mean :0.29 Mean :0.28 Mean :0.30
3rd Qu.:0.29 3rd Qu.:0.33 3rd Qu.:0.35 3rd Qu.:0.35
Max. :0.52 Max. :0.52 Max. :0.44 Max. :0.43
Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9. Soil.OutputLayers.SWmm.10. Soil.OutputLayers.SWmm.11.
Min. :0.22 Min. :63.00 Min. :64.01 Min. :64.00
1st Qu.:0.22 1st Qu.:63.34 1st Qu.:64.33 1st Qu.:64.00
Median :0.31 Median :63.70 Median :64.77 Median :64.00
Mean :0.30 Mean :63.66 Mean :64.87 Mean :64.35
3rd Qu.:0.37 3rd Qu.:63.99 3rd Qu.:65.40 3rd Qu.:64.38
Max. :0.43 Max. :64.21 Max. :66.00 Max. :66.00
Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13. Soil.OutputLayers.SWmm.14.
Min. :60.10 Min. :58.00 Min. :54.00
1st Qu.:60.15 1st Qu.:58.00 1st Qu.:54.01
Median :60.29 Median :58.00 Median :54.20
Mean :60.69 Mean :58.00 Mean :54.23
3rd Qu.:61.13 3rd Qu.:58.00 3rd Qu.:54.41
Max. :62.01 Max. :58.04 Max. :54.65
Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17.
Min. :54.00 Min. :54.00 Min. :54.50
1st Qu.:54.01 1st Qu.:54.34 1st Qu.:54.57
Median :54.06 Median :54.43 Median :54.67
Mean :54.05 Mean :54.40 Mean :55.06
3rd Qu.:54.08 3rd Qu.:54.49 3rd Qu.:55.89
Max. :54.12 Max. :54.52 Max. :56.28
Soil.OutputLayers.SWmm.18. Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20.
Min. :58.01 Min. :60.84 Min. :61.20
1st Qu.:58.81 1st Qu.:61.69 1st Qu.:62.48
Median :59.10 Median :62.50 Median :63.82
Mean :59.03 Mean :62.46 Mean :63.96
3rd Qu.:59.33 3rd Qu.:63.30 3rd Qu.:65.86
Max. :59.41 Max. :63.99 Max. :66.50
Soil.OutputLayers.SWmm.21. Soil.OutputLayers.SWmm.22. Soil.OutputLayers.SWmm.23.
Min. :58.82 Min. :51.15 Min. :0
1st Qu.:60.49 1st Qu.:51.55 1st Qu.:0
Median :63.13 Median :53.14 Median :0
Mean :63.65 Mean :56.05 Mean :0
3rd Qu.:66.49 3rd Qu.:61.78 3rd Qu.:0
Max. :71.95 Max. :64.65 Max. :0
Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10. Soil.OutputLayers.SW.11. Soil.OutputLayers.SW.12.
Min. :0.32 Min. :0.32 Min. :0.32 Min. :0.30
1st Qu.:0.32 1st Qu.:0.32 1st Qu.:0.32 1st Qu.:0.30
Median :0.32 Median :0.32 Median :0.32 Median :0.30
Mean :0.32 Mean :0.32 Mean :0.32 Mean :0.30
3rd Qu.:0.32 3rd Qu.:0.33 3rd Qu.:0.32 3rd Qu.:0.31
Max. :0.32 Max. :0.33 Max. :0.33 Max. :0.31
Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14. Soil.OutputLayers.SW.15. Soil.OutputLayers.SW.16.
Min. :0.29 Min. :0.27 Min. :0.27 Min. :0.27
1st Qu.:0.29 1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.27
Median :0.29 Median :0.27 Median :0.27 Median :0.27
Mean :0.29 Mean :0.27 Mean :0.27 Mean :0.27
3rd Qu.:0.29 3rd Qu.:0.27 3rd Qu.:0.27 3rd Qu.:0.27
Max. :0.29 Max. :0.27 Max. :0.27 Max. :0.27
Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18. Soil.OutputLayers.SW.19. Soil.OutputLayers.SW.20.
Min. :0.27 Min. :0.29 Min. :0.30 Min. :0.31
1st Qu.:0.27 1st Qu.:0.29 1st Qu.:0.31 1st Qu.:0.31
Median :0.27 Median :0.30 Median :0.31 Median :0.32
Mean :0.28 Mean :0.30 Mean :0.31 Mean :0.32
3rd Qu.:0.28 3rd Qu.:0.30 3rd Qu.:0.32 3rd Qu.:0.33
Max. :0.28 Max. :0.30 Max. :0.32 Max. :0.33
Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22. Soil.OutputLayers.SW.23. SWmm.24.
Min. :0.29 Min. :0.26 Min. :0 Min. :58.00
1st Qu.:0.30 1st Qu.:0.26 1st Qu.:0 1st Qu.:58.63
Median :0.32 Median :0.27 Median :0 Median :60.00
Mean :0.32 Mean :0.28 Mean :0 Mean :59.37
3rd Qu.:0.33 3rd Qu.:0.31 3rd Qu.:0 3rd Qu.:60.00
Max. :0.36 Max. :0.32 Max. :0 Max. :60.00
SWmm.25. SWmm.26. SWmm.27. Lucerne.Root.LengthDensity.24.
Min. :58.00 Min. :58.00 Min. :58.00 Min. :0
1st Qu.:60.00 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:0
Median :60.00 Median :60.00 Median :60.00 Median :0
Mean :59.76 Mean :59.75 Mean :59.75 Mean :0
3rd Qu.:60.00 3rd Qu.:60.00 3rd Qu.:60.00 3rd Qu.:0
Max. :60.00 Max. :60.00 Max. :60.00 Max. :0
Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26. Lucerne.Root.LengthDensity.27.
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :0 Max. :0 Max. :0
Name FolderName
Length:62472 Length:62472
Class :character Class :character
Mode :character Mode :character
[ reached getOption("max.print") -- omitted 1 row ]# myDb %>%
# dplyr::select(Name) %>%
# unique()Prepare merge
Add info for merging
select variables that are for comparing with observed data
simD <- myDb %>%
dplyr::select(Name,Clock.Today,LAI,Height,shootbiomass,RootWt, StemWt, LeafWt,NodeNumber) %>%
tidyr::gather("Variable","Predicted",LAI:NodeNumber) %>%
mutate(Name = as.factor(Name)) %>%
mutate(Variable = as.factor(Variable)) %>%
mutate(Clock.Today = ymd_hms(Clock.Today))
head(simD)summary(simD) Name Clock.Today Variable
RutherglenDefoliationCutting: 17892 Min. :1979-01-01 12:00:00 Height :62472
WarraSowingDateSD1 : 14014 1st Qu.:1999-03-31 12:00:00 LAI :62472
HudsonDefoliationCutting : 13986 Median :2001-12-04 12:00:00 LeafWt :62472
Iversen_8Waterdry : 13258 Mean :2004-04-18 16:01:43 NodeNumber :62472
Iversen_8Waterirr : 13258 3rd Qu.:2012-02-19 12:00:00 RootWt :62472
TamworthDefoliationCutting : 11557 Max. :2018-04-21 12:00:00 shootbiomass:62472
(Other) :353339 StemWt :62472
Predicted
Min. : 0.00
1st Qu.: 7.26
Median : 182.84
Mean : 1432.30
3rd Qu.: 1567.86
Max. :38562.51
head(obsNode)mergedf<-merge(obsA,simD,by=c("Clock.Today","Name","Variable"))
summary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :792 1997_2001:728
1st Qu.:2001-01-26 Iversen_121DefoliationLLFDFD5 :418 Height :697 2000_2002:926
Median :2001-11-09 Iversen_8Waterdry :367 shootbiomass:585 2002_2004:586
Mean :2004-12-10 Iversen_8Waterirr :361 LAI :538 2010_2012: 0
3rd Qu.:2004-05-31 Iversen_9SowingDateSD1Waterirr:216 RootWt :110 2014_2018:679
Max. :2018-01-15 Iversen_9SowingDateSD1Waterdry:211 LeafWt : 99
(Other) :760 (Other) : 98
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:998 dry: 831 HH: 192 No :1314 FD10: 35 00_01 :849 Min. :1.000
Lincoln2015:677 irr:2088 LL:2727 no : 679 FD2 : 34 01_02 :394 1st Qu.:1.000
Lincoln2001:323 SD1 : 427 FD5 :2850 02_03 :274 Median :2.000
Lincoln2003:286 SD2 : 186 03_04 :273 Mean :2.255
Lincoln2004:178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1999:135 SD4 : 155 16_17 :201 Max. :6.000
(Other) :322 (Other): 0 (Other):686
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-12-05 1st Qu.:2001-01-19 1st Qu.:2001-03-06 1st Qu.: 20.00
Median :2.000 Median :2001-09-30 Median :2001-10-22 Median :2001-11-14 Median : 33.00
Mean :2.922 Mean :2004-11-02 Mean :2004-12-03 Mean :2005-01-04 Mean : 38.22
3rd Qu.:4.000 3rd Qu.:2004-05-03 3rd Qu.:2004-05-22 3rd Qu.:2004-06-11 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:2919 Min. : 0.000 Min. : 0.00 E1:728 ILL :947
cm :454 1st Qu.: 4.768 1st Qu.: 0.00 E2:926 ILLF5 :418
fractio0l:575 Median : 12.580 Median : 0.39 E3:586 DLL :367
Fraction :217 Mean : 668.640 Mean : 104.97 E4: 0 ILLS1 :216
kg/ha :892 3rd Qu.: 580.665 3rd Qu.: 49.35 E5:679 DLLS1 :211
m2/m2 :538 Max. :8556.375 Max. :2532.53 IHHF5 :192
mm :243 NA's :650 (Other):568
ID GrowthSeason1 Rotation1 ExpUnitCode
E3ILL :586 Gs_1:1006 Rt_1:897 Iversen_91DefoliationLLGs_2Rt_1: 59
E5ILLF5:418 Gs_2:1033 Rt_2:594 Iversen_91DefoliationLLGs_1Rt_1: 48
E1DLL :367 Gs_3: 352 Rt_3:420 Iversen_91DefoliationLLGs_1Rt_4: 42
E1ILL :361 Gs_4: 211 Rt_4:318 Iversen_91DefoliationLLGs_1Rt_5: 42
E2ILLS1:216 Gs_5: 293 Rt_5:316 Iversen_91DefoliationLLGs_1Rt_3: 41
E2DLLS1:211 Gs_6: 24 Rt_6:285 Iversen_91DefoliationLLGs_2Rt_3: 40
(Other):760 Rt_7: 89 (Other) :2647
Clock.Today1 GrowthSeason2 Rotation2 Predicted
Min. :1997-01-28 12:00:00 Y1(00_01):556 S1 :602 Min. : 0.000
1st Qu.:2001-01-26 12:00:00 Y2(01_02):370 R2 :594 1st Qu.: 5.681
Median :2001-11-09 12:00:00 Y5(00_01):293 R3 :420 Median : 39.495
Mean :2004-12-11 02:31:57 Y1(02_03):274 R4 :318 Mean : 577.349
3rd Qu.:2004-05-31 12:00:00 Y2(03_04):273 R5 :316 3rd Qu.: 528.227
Max. :2018-01-15 12:00:00 Y2(15_16):242 R1 :295 Max. :6211.717
(Other) :911 (Other):374 str(mergedf)'data.frame': 2919 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 527.85 527.85 5.33 5.33 4165.14 ...mergedfNode number
Time series
obs Vs Pre for each experiment
1997-2001
obsNode1<-obsA%>%
dplyr::filter(Collection=="1997_2001")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_8Waterirr")%>%
dplyr::filter(Clock.Today>"1997-08-01")%>%
dplyr::filter(Clock.Today<"2001-08-01")
simD1<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"1997-08-01")%>%
dplyr::filter(Clock.Today<"2001-08-01")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_8Waterirr")
str(simD1)'data.frame': 1460 obs. of 4 variables:
$ Name : Factor w/ 73 levels "AshleyDeneSowingDateSD1",..: 35 35 35 35 35 35 35 35 35 35 ...
$ Clock.Today: POSIXct, format: "1997-10-14 12:00:00" "1998-12-19 12:00:00" "1998-03-15 12:00:00" ...
$ Variable : Factor w/ 7 levels "Height","LAI",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Predicted : num 2.089 1.414 0.691 1.466 0.335 ... simD1%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~ID,ncol = 2)+
geom_point(data=obsNode1, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))+
annotate("text", x=ymd_hms("1999-10-24 12:00:00"), y=17, size = 5, label ="paste(R_RMSD == 23,'%')", parse=T)
ggsave("D:/R/Pictures/Node/Iversen_8Waterirrnodenumber.png", width=8, height=4, dpi=500)2002-2004
obsNode2<-obsNode%>%
dplyr::filter(Collection=="2002_2004")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
simD2<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"2002-06-01")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
str(simD2)'data.frame': 884 obs. of 4 variables:
$ Name : Factor w/ 73 levels "AshleyDeneSowingDateSD1",..: 36 36 36 36 36 36 36 36 36 36 ...
$ Clock.Today: POSIXct, format: "2002-06-09 12:00:00" "2004-01-20 12:00:00" "2002-06-11 12:00:00" ...
$ Variable : Factor w/ 7 levels "Height","LAI",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Predicted : num 12.79 8.76 13.17 5.04 7.14 ...simD2%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
#facet_wrap(~ID,ncol = 1)+
geom_point(data=obsNode2, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))+
annotate("text", x=ymd_hms("2003-07-24 12:00:00"), y=17, size = 5, label ="paste(R_RMSD == 22.1,'%')", parse=T)
ggsave("D:/R/Pictures/Node/Iversen_91DefoliationLLnodenumber.png", width=8, height=4, dpi=500)
ggsave("D:/R/Pictures/Poster/E3.png", width=8, height=4, dpi=500)
2000-2002
obsNode3<-obsNode%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Variable=="NodeNumber")
obsNode3 simD3a<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"2000-10-24 12:00:00")%>%
dplyr::filter(Clock.Today<"2002-07-01 12:00:00")%>%
dplyr::filter(Name==c("Iversen_9SowingDateSD1Waterirr","Iversen_9SowingDateSD2Waterirr","Iversen_9SowingDateSD3Waterirr","Iversen_9SowingDateSD4Waterirr"))%>%
dplyr::filter(Variable=="NodeNumber")longer object length is not a multiple of shorter object lengthlonger object length is not a multiple of shorter object lengthDF<-data.frame(Name=c("Iversen_9SowingDateSD1Waterirr","Iversen_9SowingDateSD2Waterirr","Iversen_9SowingDateSD3Waterirr","Iversen_9SowingDateSD4Waterirr"),ID= c("E2ILLS1","E2ILLS2","E2ILLS3","E2ILLS4"))
simD3<-merge(DF,simD3a, by=c("Name"))
G<-simD3%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
geom_point(data=obsNode3, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
facet_wrap(~ID,ncol = 2)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
dat_text1 <- data.frame(
label = c("R_RMSE=22%", "R_RMSE=24.9%", "R_RMSE=23.1%","R_RMSE=18.7%"),
ID= c("E2ILLS1","E2ILLS2","E2ILLS3","E2ILLS4"),
x= ymd_hms("2001-03-24 12:00:00", "2001-03-24 12:00:00","2001-03-24 12:00:00","2001-03-24 12:00:00"),
y=c(18,18,18,18))
G+geom_text(data=dat_text1, mapping = aes(x=x,y=y, label = label),hjust = -0.1,vjust = -1,size=5)
ggsave("D:/R/Pictures/Node/Iversen_9SowingDatenodenumber.png", width=8, height=8, dpi=500)
2014-2018
obsNode4<-obsNode%>%
dplyr::filter(Collection=="2014_2018")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")
simD4a<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
dplyr::filter(Clock.Today<"2015-01-06")
simD4b<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
dplyr::filter(Clock.Today>"2015-01-30")
simD4<-rbind(simD4a,simD4b)
str(simD4)'data.frame': 1268 obs. of 4 variables:
$ Name : Factor w/ 73 levels "AshleyDeneSowingDateSD1",..: 20 20 20 20 20 20 20 20 20 20 ...
$ Clock.Today: POSIXct, format: "2014-10-08 12:00:00" "2014-10-09 12:00:00" "2014-10-10 12:00:00" ...
$ Variable : Factor w/ 7 levels "Height","LAI",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Predicted : num 0 0 0 0 0 0 0 0 0 0 ...simD4%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~ID,ncol = 1)+
geom_point(data=obsNode4, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))+
annotate("text", x=ymd_hms("2016-07-24 12:00:00"), y=18, size = 5, label ="paste(R_RMSD == 26.3,'%')", parse=T)
ggsave("D:/R/Pictures/Node/Iversen_121DefoliationLLFDFD5nodenumber.png", width=8, height=4, dpi=500)
Statistic and Graph
mergedfsummary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :792 1997_2001:728
1st Qu.:2001-01-26 Iversen_121DefoliationLLFDFD5 :418 Height :697 2000_2002:926
Median :2001-11-09 Iversen_8Waterdry :367 shootbiomass:585 2002_2004:586
Mean :2004-12-10 Iversen_8Waterirr :361 LAI :538 2010_2012: 0
3rd Qu.:2004-05-31 Iversen_9SowingDateSD1Waterirr:216 RootWt :110 2014_2018:679
Max. :2018-01-15 Iversen_9SowingDateSD1Waterdry:211 LeafWt : 99
(Other) :760 (Other) : 98
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:998 dry: 831 HH: 192 No :1314 FD10: 35 00_01 :849 Min. :1.000
Lincoln2015:677 irr:2088 LL:2727 no : 679 FD2 : 34 01_02 :394 1st Qu.:1.000
Lincoln2001:323 SD1 : 427 FD5 :2850 02_03 :274 Median :2.000
Lincoln2003:286 SD2 : 186 03_04 :273 Mean :2.255
Lincoln2004:178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1999:135 SD4 : 155 16_17 :201 Max. :6.000
(Other) :322 (Other): 0 (Other):686
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-12-05 1st Qu.:2001-01-19 1st Qu.:2001-03-06 1st Qu.: 20.00
Median :2.000 Median :2001-09-30 Median :2001-10-22 Median :2001-11-14 Median : 33.00
Mean :2.922 Mean :2004-11-02 Mean :2004-12-03 Mean :2005-01-04 Mean : 38.22
3rd Qu.:4.000 3rd Qu.:2004-05-03 3rd Qu.:2004-05-22 3rd Qu.:2004-06-11 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:2919 Min. : 0.000 Min. : 0.00 E1:728 ILL :947
cm :454 1st Qu.: 4.768 1st Qu.: 0.00 E2:926 ILLF5 :418
fractio0l:575 Median : 12.580 Median : 0.39 E3:586 DLL :367
Fraction :217 Mean : 668.640 Mean : 104.97 E4: 0 ILLS1 :216
kg/ha :892 3rd Qu.: 580.665 3rd Qu.: 49.35 E5:679 DLLS1 :211
m2/m2 :538 Max. :8556.375 Max. :2532.53 IHHF5 :192
mm :243 NA's :650 (Other):568
ID GrowthSeason1 Rotation1 ExpUnitCode
E3ILL :586 Gs_1:1006 Rt_1:897 Iversen_91DefoliationLLGs_2Rt_1: 59
E5ILLF5:418 Gs_2:1033 Rt_2:594 Iversen_91DefoliationLLGs_1Rt_1: 48
E1DLL :367 Gs_3: 352 Rt_3:420 Iversen_91DefoliationLLGs_1Rt_4: 42
E1ILL :361 Gs_4: 211 Rt_4:318 Iversen_91DefoliationLLGs_1Rt_5: 42
E2ILLS1:216 Gs_5: 293 Rt_5:316 Iversen_91DefoliationLLGs_1Rt_3: 41
E2DLLS1:211 Gs_6: 24 Rt_6:285 Iversen_91DefoliationLLGs_2Rt_3: 40
(Other):760 Rt_7: 89 (Other) :2647
Clock.Today1 GrowthSeason2 Rotation2 Predicted
Min. :1997-01-28 12:00:00 Y1(00_01):556 S1 :602 Min. : 0.000
1st Qu.:2001-01-26 12:00:00 Y2(01_02):370 R2 :594 1st Qu.: 5.681
Median :2001-11-09 12:00:00 Y5(00_01):293 R3 :420 Median : 39.495
Mean :2004-12-11 02:31:57 Y1(02_03):274 R4 :318 Mean : 577.349
3rd Qu.:2004-05-31 12:00:00 Y2(03_04):273 R5 :316 3rd Qu.: 528.227
Max. :2018-01-15 12:00:00 Y2(15_16):242 R1 :295 Max. :6211.717
(Other) :911 (Other):374 str(mergedf)'data.frame': 2919 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 527.85 527.85 5.33 5.33 4165.14 ... d<-mergedf %>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
dplyr::filter(Collection!="2010_2012")%>%
ggplot(aes(x=Observed, y= Predicted, colour= factor(Name))) +
geom_point(size=2)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_wrap(~ID, ncol = 4)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
d
ggsave("D:/R/Pictures/Node/nodenumber.png", width=8, height=6, dpi=500)# # fit model and get lower/upper 95% confidence limits at the observed x's
# m <- lm(yvalues ~ xvalues, data = d)
# mp <- predict(m, interval = 'conf')
#
# # bind predictions to original data and plot
# d2 <- cbind(d, mp)
# p <- ggplot(d2, aes(x = xvalues, y = yvalues))
# p + geom_point() + geom_smooth(method = 'lm', se = FALSE) +
# geom_line(aes(y = upr), color = 'red', linetype = 2) +
# geom_line(aes(y = lwr), color = 'red', linetype = 2)
mergedf1<-mergedf%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
dplyr::filter(Collection!="2010_2012")%>%
dplyr::filter(ID==c("E3ILL","E5ILLF5"))longer object length is not a multiple of shorter object lengthlonger object length is not a multiple of shorter object lengthsummary(mergedf1) Clock.Today Name Variable Collection
Min. :2002-08-28 Iversen_121DefoliationLLFDFD5 :60 NodeNumber:105 1997_2001: 0
1st Qu.:2003-10-21 Iversen_91DefoliationLL :45 Branch : 0 2000_2002: 0
Median :2014-12-30 Iversen_121DefoliationHHFDFD5 : 0 CrownWt : 0 2002_2004:45
Mean :2010-11-09 Iversen_121DefoliationLLFDFD10: 0 Fraction : 0 2010_2012: 0
3rd Qu.:2016-05-10 Iversen_121DefoliationLLFDFD2 : 0 HardStemWt: 0 2014_2018:60
Max. :2017-12-29 Iversen_12SowingDateSD1 : 0 Height : 0
(Other) : 0 (Other) : 0
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2015:60 dry: 0 HH: 0 no :60 FD10: 0 02_03 :21 Min. :1.000
Lincoln2003:22 irr:105 LL:105 No :45 FD2 : 0 03_04 :21 1st Qu.:1.000
Lincoln2004:14 SD1 : 0 FD5 :105 16_17 :20 Median :2.000
Lincoln2002: 9 SD10 : 0 14_15 :17 Mean :1.971
Lincoln1997: 0 SD2 : 0 15_16 :17 3rd Qu.:3.000
Lincoln1998: 0 SD3 : 0 17_18 : 6 Max. :4.000
(Other) : 0 (Other): 0 (Other): 3
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :2002-06-14 Min. :2002-08-09 Min. :2002-10-05 Min. : 0.0
1st Qu.:1.000 1st Qu.:2003-10-06 1st Qu.:2003-10-24 1st Qu.:2003-11-12 1st Qu.: 12.0
Median :3.000 Median :2014-11-03 Median :2014-12-16 Median :2015-01-29 Median : 25.0
Mean :3.257 Mean :2010-10-11 Mean :2010-11-07 Mean :2010-12-05 Mean : 28.7
3rd Qu.:5.000 3rd Qu.:2016-04-08 3rd Qu.:2016-04-28 3rd Qu.:2016-05-18 3rd Qu.: 37.0
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :111.0
VariableUnits Time Observed StdDEV ExperimentID TreatmentID ID
% : 0 12:00:00:105 Min. : 0.000 Min. :0.1000 E1: 0 ILLF5 :60 E5ILLF5:60
cm : 0 1st Qu.: 4.000 1st Qu.:0.3300 E2: 0 ILL :45 E3ILL :45
fractio0l:45 Median : 6.900 Median :0.4300 E3:45 DLL : 0 E1DLL : 0
Fraction :60 Mean : 6.853 Mean :0.4726 E4: 0 DLLS1 : 0 E1ILL : 0
kg/ha : 0 3rd Qu.: 9.600 3rd Qu.:0.6200 E5:60 DLLS10 : 0 E2DLLS1: 0
m2/m2 : 0 Max. :14.610 Max. :1.0200 DLLS2 : 0 E2DLLS2: 0
mm : 0 NA's :52 (Other): 0 (Other): 0
GrowthSeason1 Rotation1 ExpUnitCode Clock.Today1
Gs_1:38 Rt_1:28 Iversen_121DefoliationLLFDFD5Gs_1Rt_1: 8 Min. :2002-08-28 12:00:00
Gs_2:38 Rt_2:16 Iversen_91DefoliationLLGs_2Rt_1 : 5 1st Qu.:2003-10-21 12:00:00
Gs_3:23 Rt_3:16 Iversen_91DefoliationLLGs_1Rt_5 : 4 Median :2014-12-30 12:00:00
Gs_4: 6 Rt_4:15 Iversen_121DefoliationLLFDFD5Gs_3Rt_1: 4 Mean :2010-11-09 21:03:25
Gs_5: 0 Rt_5:12 Iversen_91DefoliationLLGs_1Rt_1 : 3 3rd Qu.:2016-05-10 12:00:00
Gs_6: 0 Rt_6:12 Iversen_91DefoliationLLGs_1Rt_2 : 3 Max. :2017-12-29 12:00:00
Rt_7: 6 (Other) :78
GrowthSeason2 Rotation2 Predicted
Y1(02_03):21 S1 :17 Min. : 0.1143
Y2(03_04):21 R2 :16 1st Qu.: 3.1013
Y3(16_17):20 R3 :16 Median : 6.2422
Y1(14_15):17 R4 :15 Mean : 6.2490
Y2(15_16):17 R5 :12 3rd Qu.: 8.8380
Y4(17_18): 6 R6 :12 Max. :15.0196
(Other) : 3 (Other):17 str(mergedf1)'data.frame': 105 obs. of 30 variables:
$ Clock.Today : Date, format: "2002-08-28" "2002-09-11" "2002-09-26" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 17 17 17 17 17 17 17 17 17 17 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 8 8 8 8 8 8 8 8 8 8 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 3 3 3 3 3 3 3 3 3 3 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 6 6 6 6 6 6 6 6 6 7 ...
$ Water : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 1 1 ...
$ Rotation : int 1 1 1 2 2 2 3 3 3 4 ...
$ StartDate : Date, format: "2002-06-14" "2002-06-14" "2002-06-14" ...
$ MidDate : Date, format: "2002-08-09" "2002-08-09" "2002-08-09" ...
$ FinishDate : Date, format: "2002-10-05" "2002-10-05" "2002-10-05" ...
$ Interval : int 75 89 104 11 23 33 10 19 35 12 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 3 3 3 3 3 3 3 3 3 3 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 6.9 8.6 11.25 3.18 6.9 ...
$ StdDEV : num 0.26 0.43 0.53 0.33 0.42 0.44 0.1 0.3 0.77 0.38 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 3 3 3 3 3 3 3 3 3 3 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 13 13 13 13 13 13 13 13 13 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 11 11 11 11 11 11 11 11 11 11 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 2 2 2 3 3 3 4 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 61 61 61 62 62 62 63 63 63 64 ...
$ Clock.Today1 : POSIXct, format: "2002-08-28 12:00:00" "2002-09-11 12:00:00" "2002-09-26 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 7 7 7 7 7 7 7 7 7 7 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 2 2 2 3 3 3 4 ...
$ Predicted : num 8.46 11.24 14.28 1.79 4.43 ...m <- lm(Predicted ~ Observed, data = mergedf1)
mp <- predict(m, interval = 'conf')
d2 <- cbind(mergedf1, mp)
p1<-d2 %>%
ggplot(aes(x=Observed, y= Predicted,color=ID))+
geom_point(size=2,colour="blue")+theme_bw()+ geom_smooth(method = 'lm', se = FALSE, color="black") +
geom_line(aes(y = upr), color = 'black', linetype = 3,size=1) +
geom_line(aes(y = lwr), color = 'black', linetype = 3,size=1)+
#geom_smooth(method = "lm", se = TRUE, linetype = 3, colour="black") +
geom_abline(intercept = 0, slope = 1,size=1,color="purple") +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
#facet_wrap(~ID, ncol = 4)+
theme(legend.title=element_blank(),legend.position = "blank")+
xlab("Observed")+ylab("Predicted")+
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
ggsave("D:/R/Pictures/Poster/nodenumber.png", width=8, height=4, dpi=500)2002-2004
mergedfsummary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :792 1997_2001:728
1st Qu.:2001-01-26 Iversen_121DefoliationLLFDFD5 :418 Height :697 2000_2002:926
Median :2001-11-09 Iversen_8Waterdry :367 shootbiomass:585 2002_2004:586
Mean :2004-12-10 Iversen_8Waterirr :361 LAI :538 2010_2012: 0
3rd Qu.:2004-05-31 Iversen_9SowingDateSD1Waterirr:216 RootWt :110 2014_2018:679
Max. :2018-01-15 Iversen_9SowingDateSD1Waterdry:211 LeafWt : 99
(Other) :760 (Other) : 98
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:998 dry: 831 HH: 192 No :1314 FD10: 35 00_01 :849 Min. :1.000
Lincoln2015:677 irr:2088 LL:2727 no : 679 FD2 : 34 01_02 :394 1st Qu.:1.000
Lincoln2001:323 SD1 : 427 FD5 :2850 02_03 :274 Median :2.000
Lincoln2003:286 SD2 : 186 03_04 :273 Mean :2.255
Lincoln2004:178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1999:135 SD4 : 155 16_17 :201 Max. :6.000
(Other) :322 (Other): 0 (Other):686
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-12-05 1st Qu.:2001-01-19 1st Qu.:2001-03-06 1st Qu.: 20.00
Median :2.000 Median :2001-09-30 Median :2001-10-22 Median :2001-11-14 Median : 33.00
Mean :2.922 Mean :2004-11-02 Mean :2004-12-03 Mean :2005-01-04 Mean : 38.22
3rd Qu.:4.000 3rd Qu.:2004-05-03 3rd Qu.:2004-05-22 3rd Qu.:2004-06-11 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:2919 Min. : 0.000 Min. : 0.00 E1:728 ILL :947
cm :454 1st Qu.: 4.768 1st Qu.: 0.00 E2:926 ILLF5 :418
fractio0l:575 Median : 12.580 Median : 0.39 E3:586 DLL :367
Fraction :217 Mean : 668.640 Mean : 104.97 E4: 0 ILLS1 :216
kg/ha :892 3rd Qu.: 580.665 3rd Qu.: 49.35 E5:679 DLLS1 :211
m2/m2 :538 Max. :8556.375 Max. :2532.53 IHHF5 :192
mm :243 NA's :650 (Other):568
ID GrowthSeason1 Rotation1 ExpUnitCode
E3ILL :586 Gs_1:1006 Rt_1:897 Iversen_91DefoliationLLGs_2Rt_1: 59
E5ILLF5:418 Gs_2:1033 Rt_2:594 Iversen_91DefoliationLLGs_1Rt_1: 48
E1DLL :367 Gs_3: 352 Rt_3:420 Iversen_91DefoliationLLGs_1Rt_4: 42
E1ILL :361 Gs_4: 211 Rt_4:318 Iversen_91DefoliationLLGs_1Rt_5: 42
E2ILLS1:216 Gs_5: 293 Rt_5:316 Iversen_91DefoliationLLGs_1Rt_3: 41
E2DLLS1:211 Gs_6: 24 Rt_6:285 Iversen_91DefoliationLLGs_2Rt_3: 40
(Other):760 Rt_7: 89 (Other) :2647
Clock.Today1 GrowthSeason2 Rotation2 Predicted
Min. :1997-01-28 12:00:00 Y1(00_01):556 S1 :602 Min. : 0.000
1st Qu.:2001-01-26 12:00:00 Y2(01_02):370 R2 :594 1st Qu.: 5.681
Median :2001-11-09 12:00:00 Y5(00_01):293 R3 :420 Median : 39.495
Mean :2004-12-11 02:31:57 Y1(02_03):274 R4 :318 Mean : 577.349
3rd Qu.:2004-05-31 12:00:00 Y2(03_04):273 R5 :316 3rd Qu.: 528.227
Max. :2018-01-15 12:00:00 Y2(15_16):242 R1 :295 Max. :6211.717
(Other) :911 (Other):374 str(mergedf)'data.frame': 2919 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 527.85 527.85 5.33 5.33 4165.14 ...mergedf %>%
dplyr::filter(Collection=="2002_2004")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="2014_2018")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
2000-2002
mergedfsummary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :792 1997_2001:728
1st Qu.:2001-01-26 Iversen_121DefoliationLLFDFD5 :418 Height :697 2000_2002:926
Median :2001-11-09 Iversen_8Waterdry :367 shootbiomass:585 2002_2004:586
Mean :2004-12-10 Iversen_8Waterirr :361 LAI :538 2010_2012: 0
3rd Qu.:2004-05-31 Iversen_9SowingDateSD1Waterirr:216 RootWt :110 2014_2018:679
Max. :2018-01-15 Iversen_9SowingDateSD1Waterdry:211 LeafWt : 99
(Other) :760 (Other) : 98
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:998 dry: 831 HH: 192 No :1314 FD10: 35 00_01 :849 Min. :1.000
Lincoln2015:677 irr:2088 LL:2727 no : 679 FD2 : 34 01_02 :394 1st Qu.:1.000
Lincoln2001:323 SD1 : 427 FD5 :2850 02_03 :274 Median :2.000
Lincoln2003:286 SD2 : 186 03_04 :273 Mean :2.255
Lincoln2004:178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1999:135 SD4 : 155 16_17 :201 Max. :6.000
(Other) :322 (Other): 0 (Other):686
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-12-05 1st Qu.:2001-01-19 1st Qu.:2001-03-06 1st Qu.: 20.00
Median :2.000 Median :2001-09-30 Median :2001-10-22 Median :2001-11-14 Median : 33.00
Mean :2.922 Mean :2004-11-02 Mean :2004-12-03 Mean :2005-01-04 Mean : 38.22
3rd Qu.:4.000 3rd Qu.:2004-05-03 3rd Qu.:2004-05-22 3rd Qu.:2004-06-11 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:2919 Min. : 0.000 Min. : 0.00 E1:728 ILL :947
cm :454 1st Qu.: 4.768 1st Qu.: 0.00 E2:926 ILLF5 :418
fractio0l:575 Median : 12.580 Median : 0.39 E3:586 DLL :367
Fraction :217 Mean : 668.640 Mean : 104.97 E4: 0 ILLS1 :216
kg/ha :892 3rd Qu.: 580.665 3rd Qu.: 49.35 E5:679 DLLS1 :211
m2/m2 :538 Max. :8556.375 Max. :2532.53 IHHF5 :192
mm :243 NA's :650 (Other):568
ID GrowthSeason1 Rotation1 ExpUnitCode
E3ILL :586 Gs_1:1006 Rt_1:897 Iversen_91DefoliationLLGs_2Rt_1: 59
E5ILLF5:418 Gs_2:1033 Rt_2:594 Iversen_91DefoliationLLGs_1Rt_1: 48
E1DLL :367 Gs_3: 352 Rt_3:420 Iversen_91DefoliationLLGs_1Rt_4: 42
E1ILL :361 Gs_4: 211 Rt_4:318 Iversen_91DefoliationLLGs_1Rt_5: 42
E2ILLS1:216 Gs_5: 293 Rt_5:316 Iversen_91DefoliationLLGs_1Rt_3: 41
E2DLLS1:211 Gs_6: 24 Rt_6:285 Iversen_91DefoliationLLGs_2Rt_3: 40
(Other):760 Rt_7: 89 (Other) :2647
Clock.Today1 GrowthSeason2 Rotation2 Predicted
Min. :1997-01-28 12:00:00 Y1(00_01):556 S1 :602 Min. : 0.000
1st Qu.:2001-01-26 12:00:00 Y2(01_02):370 R2 :594 1st Qu.: 5.681
Median :2001-11-09 12:00:00 Y5(00_01):293 R3 :420 Median : 39.495
Mean :2004-12-11 02:31:57 Y1(02_03):274 R4 :318 Mean : 577.349
3rd Qu.:2004-05-31 12:00:00 Y2(03_04):273 R5 :316 3rd Qu.: 528.227
Max. :2018-01-15 12:00:00 Y2(15_16):242 R1 :295 Max. :6211.717
(Other) :911 (Other):374 str(mergedf)'data.frame': 2919 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 527.85 527.85 5.33 5.33 4165.14 ...mergedf %>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank())+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="1997_2001")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw() +
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1) +
ggtitle("Node Number") +
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="1997_2001")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw() +
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1) +
ggtitle("Node Number") +
# facet_grid(.~Rotation) + # Rotation
facet_wrap(~GrowthSeason, ncol = 4)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
RMSE
str(mergedf)'data.frame': 2919 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 527.85 527.85 5.33 5.33 4165.14 ...mergedf %>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
#filter(Variable == "NodeNumber") %>%
filter(Collection!="2010_2012")%>%
mutate(Rotation= as.factor(Rotation))%>%
mutate(GrowthSeason=as.factor(GrowthSeason))%>%
group_by(ID) %>%
summarise(
n = n(),
r2 = gauchStats(Predicted,Observed)[5],
# rmse = round(rmse(Predicted,Observed),0),
r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
nse = round(NSE(Predicted,Observed),2),
sb = gauchStats(Predicted,Observed)[1],
nu = gauchStats(Predicted,Observed)[2],
lc = gauchStats(Predicted,Observed)[3]
) 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